Search results for: service performance in supply chain

Authors: Manal M. Kame, Hanan G. El-Baz, Zeinab A.Demerdash, Engy M. Abd El-Moneem, Mohamed A. Hendawy, Ibrahim R. Bayoumi

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There is a constant need to improve the performance of current diagnostic assays of schistosomiasis as well as develop innovative testing strategies to meet new testing challenges. This study aims at increasing the diagnostic efficiency of monoclonal antibody (MAb)-based antigen detection assays through gold nanoparticles conjugated with specific anti-Schistosoma mansoni monoclonal antibodies. In this study, several hybidoma cell lines secreting MAbs against adult worm tegumental Schistosoma antigen (AWTA) were produced at Immunology Department of Theodor Bilharz Research Institute and preserved in liquid nitrogen. One MAb (6D/6F) was chosen for this study due to its high reactivity to schistosome antigens with highest optical density (OD) values. Gold nanoparticles (AuNPs) were functionalized and conjugated with MAb (6D/6F). The study was conducted on serum samples of 116 subjects: 71 patients with S. mansoni eggs in their stool samples group (gp 1), 25 with other parasites (gp2) and 20 negative healthy controls (gp3). Patients in gp1 were further subdivided according to egg count in their stool samples into Light infection {≤ 50 egg per gram(epg) (n= 17)}, moderate {51-100 epg (n= 33)} and severe infection {>100 epg(n= 21)}. Sandwich ELISA was performed using (AuNPs -MAb) for detection of circulating schistosomal antigen (CSA) levels in serum samples of all groups and the results were compared with that after using MAb/ sandwich ELISA system. Results Gold- MAb/ ELISA system reached a lower detection limit of 10 ng/ml compared to 85 ng/ml on using MAb/ ELISA and the optimal concentrations of AuNPs -MAb were found to be 12 folds less than that of MAb/ ELISA system for detection of CSA. The sensitivity and specificity of sandwich ELISA for detection of CSA levels using AuNPs -MAb were 100% & 97.8 % respectively compared to 87.3% &93.38% respectively on using MAb/ ELISA system. It was found that CSA was detected in 9 out of 71 S.mansoni infected patients on using AuNPs - MAb/ ELISA system and was not detected by MAb/ ELISA system. All those patients (9) was found to have an egg count below 50 epg feces (patients with light infections). ROC curve analyses revealed that sandwich ELISA using gold-MAb was an excellent diagnostic investigator that could differentiate Schistosoma patients from healthy controls, on the other hand it revealed that sandwich ELISA using MAb was not accurate enough as it could not recognize nine out of 71 patients with light infections. Conclusion Our data demonstrated that: Loading gold nanoparticles with MAb (6D/6F) increases the sensitivity and specificity of sandwich ELISA for detection of CSA, thus active (early) and light infections could be easily detected. Moreover this binding will decrease the amount of MAb consumed in the assay and lower the coast. The significant positive correlation that was detected between ova count (intensity of infection) and OD reading in sandwich ELISA using gold- MAb enables its use to detect the severity of infections and follow up patients after treatment for monitoring of cure.

Keywords: Schistosomiasis, nanoparticles, gold, monoclonal antibodies, ELISA

Procedia PDF Downloads 370

Authors: Atchara Sankom, Warapa Mahakarnchanakul, Ronnarit Rittiron, Tanaboon Sajjaanantakul, Thammasak Thongket

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Vegetables are frequently contaminated with pesticides residues resulting in the most food safety concern among agricultural products. The objective of this work was to develop a method to detect the organophosphate (OP) pesticides residues in vegetables using Near Infrared (NIR) spectroscopy technique. Low concentration (ppm) of OP pesticides in vegetables were investigated. The experiment was divided into 2 sections. In the first section, Chinese kale spiked with different concentrations of chlorpyrifos pesticide residues (0.5-100 ppm) was chosen as the sample model to demonstrate the appropriate conditions of sample preparation, both for a solution or solid sample. The spiked samples were extracted with acetone. The sample extracts were applied as solution samples, while the solid samples were prepared by the dry-extract system for infrared (DESIR) technique. The DESIR technique was performed by embedding the solution sample on filter paper (GF/A) and then drying. The NIR spectra were measured with the transflectance mode over wavenumber regions of 12,500-4000 cm⁻¹. The QuEChERS method followed by gas chromatography-mass spectrometry (GC-MS) was performed as the standard method. The results from the first section showed that the DESIR technique with NIR spectroscopy demonstrated good accurate calibration result with R² of 0.93 and RMSEP of 8.23 ppm. However, in the case of solution samples, the prediction regarding the NIR-PLSR (partial least squares regression) equation showed poor performance (R² = 0.16 and RMSEP = 23.70 ppm). In the second section, the DESIR technique coupled with NIR spectroscopy was applied to the detection of OP pesticides in vegetables. Vegetables (Chinese kale, cabbage and hot chili) were spiked with OP pesticides (chlorpyrifos ethion and profenofos) at different concentrations ranging from 0.5 to 100 ppm. Solid samples were prepared (based on the DESIR technique), then samples were scanned by NIR spectrophotometer at ambient temperature (25+2°C). The NIR spectra were measured as in the first section. The NIR- PLSR showed the best calibration equation for detecting low concentrations of chlorpyrifos residues in vegetables (Chinese kale, cabbage and hot chili) according to the prediction set of R2 and RMSEP of 0.85-0.93 and 8.23-11.20 ppm, respectively. For ethion residues, the best calibration equation of NIR-PLSR showed good indexes of R² and RMSEP of 0.88-0.94 and 7.68-11.20 ppm, respectively. As well as the results for profenofos pesticide, the NIR-PLSR also showed the best calibration equation for detecting the profenofos residues in vegetables according to the good index of R² and RMSEP of 0.88-0.97 and 5.25-11.00 ppm, respectively. Moreover, the calibration equation developed in this work could rapidly predict the concentrations of OP pesticides residues (0.5-100 ppm) in vegetables, and there was no significant difference between NIR-predicted values and actual values (data from GC-MS) at a confidence interval of 95%. In this work, the proposed method using NIR spectroscopy involving the DESIR technique has proved to be an efficient method for the screening detection of OP pesticides residues at low concentrations, and thus increases the food safety potential of vegetables for domestic and export markets.

Keywords: NIR spectroscopy, organophosphate pesticide, vegetable, food safety

Procedia PDF Downloads 147

Authors: Boriana Karamanova, Svetlana Veleva, Luybomir Soserov, Ana Arenillas, Francesco Lufrano, Antonia Stoyanova

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Supercapacitors have received a lot of research attention and are promising energy storage devices due to their high power and long cycle life. In order to developed an advanced device with significant capacity for storing charge and cheap carbon materials, efforts must focus not only on improving synthesis by controlling the morphology and pore size but also on improving electrode-electrolyte compatibility of the resulting systems. The present study examines the relationship between the surface chemistry of two activated carbon xerogels, the electrolyte type, and the electrochemical properties of supercapacitors. Activated carbon xerogels were prepared by varying the initial pH of the resorcinol-formaldehyde aqueous solution. The materials produced are physicochemical characterized by DTA/TGA, porous characterization, and SEM analysis. The carbon xerogel based electrodes were prepared by spreading over glass plate a slurry containing the carbon gel, graphite, and poly vinylidene difluoride (PVDF) binder. The layer formed was dried consecutively at different temperatures and then detached by water. After, the layer was dried again to improve its mechanical stability. The developed electrode materials and the Aquivion® E87-05S membrane (Solvay Specialty Polymers), socked in Na2SO4 as a polymer electrolyte, were used to assembly the solid-state supercapacitor. Symmetric supercapacitor cells composed by same electrodes and 1 M KOH electrolytes are also assembled and tested for comparison. The supercapacitor performances are verified by different electrochemical methods - cyclic voltammetry, galvanostatic charge/discharge measurements, electrochemical impedance spectroscopy, and long-term durability tests in neutral and alkaline electrolytes. Specific capacitances, energy, and power density, energy efficiencies, and durability were compared into studied supercapacitors. Ex-situ physicochemical analyses on the synthesized materials have also been performed, which provide information about chemical and structural changes in the electrode morphology during charge / discharge durability tests. They are discussed on the basis of electrode-electrolyte interaction. The obtained correlations could be of significance in order to design sustainable solid-state supercapacitors with high power and energy density. Acknowledgement: This research is funded by the Ministry of Education and Science of Bulgaria under the National Program "European Scientific Networks" (Agreement D01-286 / 07.10.2020, D01-78/30.03.2021). Authors gratefully acknowledge.

Keywords: carbon xerogel, electrochemical tests, neutral and alkaline electrolytes, supercapacitors

Procedia PDF Downloads 136

Authors: Sankha Chakrabortty, Biswajit Ruj, Parimal Pal

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Methanol is one of the most important chemical products and intermediates. It can be used as a solvent, intermediate or raw material for a number of higher valued products, fuels or additives. From the last one decay, the total global demand of methanol has increased drastically which forces the scientists to produce a large amount of methanol from a renewable source to meet the global demand with a sustainable way. Different types of non-renewable based raw materials have been used for the synthesis of methanol on a large scale which makes the process unsustainable. In this circumstances, photocatalytic conversion of CO₂ into methanol under solar/UV excitation becomes a viable approach to give a sustainable production approach which not only meets the environmental crisis by recycling CO₂ to fuels but also reduces CO₂ amount from the atmosphere. Development of such sustainable production approach for CO₂ conversion into methanol still remains a major challenge in the current research comparing with conventional energy expensive processes. In this backdrop, the development of environmentally friendly materials, like photocatalyst has taken a great perspective for methanol synthesis. Scientists in this field are always concerned about finding an improved photocatalyst to enhance the photocatalytic performance. Graphene-based hybrid and composite materials with improved properties could be a better nanomaterial for the selective conversion of CO₂ to methanol under visible light (solar energy) or UV light. The present invention relates to synthesis an improved heterogeneous graphene-based photocatalyst with improved catalytic activity and surface area. Graphene with enhanced surface area is used as coupled material of copper-loaded titanium oxide to improve the electron capture and transport properties which substantially increase the photoinduced charge transfer and extend the lifetime of photogenerated charge carriers. A fast reduction method through H₂ purging has been adopted to synthesis improved graphene whereas ultrasonication based sol-gel method has been applied for the preparation of graphene coupled copper loaded titanium oxide with some enhanced properties. Prepared photocatalysts were exhaustively characterized using different characterization techniques. Effects of catalyst dose, CO₂ flow rate, reaction temperature and stirring time on the efficacy of the system in terms of methanol yield and productivity have been studied in the present study. The study shown that the newly synthesized photocatalyst with an enhanced surface resulting in a sustained productivity and yield of methanol 0.14 g/Lh, and 0.04 g/gcat respectively, after 3 h of illumination under UV (250W) at an optimum catalyst dosage of 10 g/L having 1:2:3 (Graphene: TiO₂: Cu) weight ratio.

Keywords: renewable energy, CO₂ capture, photocatalytic conversion, methanol

Procedia PDF Downloads 107

Authors: Min Han, Di Wu, Lin Yuan, Fei Liu

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Mechanical sensors have undergone a continuous evolution and have become an important part of many industries, ranging from manufacturing to process, chemicals, machinery, health-care, environmental monitoring, automotive, avionics, and household appliances. Concurrently, the microelectronics and microfabrication technology have provided us with the means of producing mechanical microsensors characterized by high sensitivity, small size, integrated electronics, on board calibration, and low cost. Here we report a new kind of mechanical sensors based on the quantum transport process of electrons in the closely spaced nanoparticle films covering a flexible polymer sheet. The nanoparticle films were fabricated by gas phase depositing of preformed metal nanoparticles with a controlled coverage on the electrodes. To amplify the conductance of the nanoparticle array, we fabricated silver interdigital electrodes on polyethylene terephthalate(PET) by mask evaporation deposition. The gaps of the electrodes ranged from 3 to 30μm. Metal nanoparticles were generated from a magnetron plasma gas aggregation cluster source and deposited on the interdigital electrodes. Closely spaced nanoparticle arrays with different coverage could be gained through real-time monitoring the conductance. In the film coulomb blockade and quantum, tunneling/hopping dominate the electronic conduction mechanism. The basic principle of the mechanical sensors relies on the mechanical deformation of the fabricated devices which are translated into electrical signals. Several kinds of sensing devices have been explored. As a strain sensor, the device showed a high sensitivity as well as a very wide dynamic range. A gauge factor as large as 100 or more was demonstrated, which can be at least one order of magnitude higher than that of the conventional metal foil gauges or even better than that of the semiconductor-based gauges with a workable maximum applied strain beyond 3%. And the strain sensors have a workable maximum applied strain larger than 3%. They provide the potential to be a new generation of strain sensors with performance superior to that of the currently existing strain sensors including metallic strain gauges and semiconductor strain gauges. When integrated into a pressure gauge, the devices demonstrated the ability to measure tiny pressure change as small as 20Pa near the atmospheric pressure. Quantitative vibration measurements were realized on a free-standing cantilever structure fabricated with closely-spaced nanoparticle array sensing element. What is more, the mechanical sensor elements can be easily scaled down, which is feasible for MEMS and NEMS applications.

Keywords: gas phase deposition, mechanical sensors, metallic nanoparticle arrays, quantum conductance

Procedia PDF Downloads 273

Authors: M. Mashiur Rahman, Ulrik Birk Henriksen, Jesper Ahrenfeldt, Maria Puig Arnavat

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Using biomass gasification to make producer gas is one of the promising sustainable energy options available for small scale plant and rural applications for power and electricity. Tar content in producer gas is the main problem if it is used directly as a fuel. A low-tar biomass (LTB) gasifier of approximately 30 kW capacity has been developed to solve this. Moving bed gasifier with internal recirculation of pyrolysis gas has been the basic principle of the LTB gasifier. The gasifier focuses on the concept of mixing the pyrolysis gases with gasifying air and burning the mixture in separate combustion chamber. Five tests were carried out with the use of wood pellets and wood chips separately, with moisture content of 9-34%. The LTB gasifier offers excellent opportunities for handling extremely low-tar in the producer gas. The gasifiers producer gas had an extremely low tar content of 21.2 mg/Nm³ (avg.) and an average lower heating value (LHV) of 4.69 MJ/Nm³. Tar content found in different tests in the ranges of 10.6-29.8 mg/Nm³. This low tar content makes the producer gas suitable for direct use in internal combustion engine. Using mass and energy balances, the average gasifier capacity and cold gas efficiency (CGE) observed 23.1 kW and 82.7% for wood chips, and 33.1 kW and 60.5% for wood pellets, respectively. Average heat loss in term of higher heating value (HHV) observed 3.2% of thermal input for wood chips and 1% for wood pellets, where heat loss was found 1% of thermal input in term of enthalpy. Thus, the LTB gasifier performs better compared to typical gasifiers in term of heat loss. Equivalence ratio (ER) in the range of 0.29 to 0.41 gives better performance in terms of heating value and CGE. The specific gas production yields at the above ER range were in the range of 2.1-3.2 Nm³/kg. Heating value and CGE changes proportionally with the producer gas yield. The average gas compositions (H₂-19%, CO-19%, CO₂-10%, CH₄-0.7% and N₂-51%) obtained for wood chips are higher than the typical producer gas composition. Again, the temperature profile of the LTB gasifier observed relatively low temperature compared to typical moving bed gasifier. The average partial oxidation zone temperature of 970°C observed for wood chips. The use of separate combustor in the partial oxidation zone substantially lowers the bed temperature to 750°C. During the test, the engine was started and operated completely with the producer gas. The engine operated well on the produced gas, and no deposits were observed in the engine afterwards. Part of the producer gas flow was used for engine operation, and corresponding electrical power was found to be 1.5 kW continuously, and maximum power of 2.5 kW was also observed, while maximum generator capacity is 3 kW. A thermodynamic equilibrium model is good agreement with the experimental results and correctly predicts the equilibrium bed temperature, gas composition, LHV of the producer gas and ER with the experimental data, when the heat loss of 4% of the energy input is considered.

Keywords: biomass gasification, low-tar biomass gasifier, tar elimination, engine, deposits, condensate

Procedia PDF Downloads 114

Authors: Dragan Ribarić

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We employ the so-called problem-dependent linked interpolation concept to develop two cubic 4-node quadrilateral Mindlin plate finite elements with 12 external degrees of freedom. In the problem-independent linked interpolation, the interpolation functions are independent of any problem material parameters and the rotation fields are not expressed in terms of the nodal displacement parameters. On the contrary, in the problem-dependent linked interpolation, the interpolation functions depend on the material parameters and the rotation fields are expressed in terms of the nodal displacement parameters. Two cubic 4-node quadrilateral plate elements are presented, named Q4-U3 and Q4-U3R5. The first one is modelled with one displacement and two rotation degrees of freedom in every of the four element nodes and the second element has five additional internal degrees of freedom to get polynomial completeness of the cubic form and which can be statically condensed within the element. Both elements are able to pass the constant-bending patch test exactly as well as the non-zero constant-shear patch test on the oriented regular mesh geometry in the case of cylindrical bending. In any mesh shape, the elements have the correct rank and only the three eigenvalues, corresponding to the solid body motions are zero. There are no additional spurious zero modes responsible for instability of the finite element models. In comparison with the problem-independent cubic linked interpolation implemented in Q9-U3, the nine-node plate element, significantly less degrees of freedom are employed in the model while retaining the interpolation conformity between adjacent elements. The presented elements are also compared to the existing problem-independent quadratic linked-interpolation element Q4-U2 and to the other known elements that also use the quadratic or the cubic linked interpolation, by testing them on several benchmark examples. Simple functional upgrading from the quadratic to the cubic linked interpolation, implemented in Q4-U3 element, showed no significant improvement compared to the quadratic linked form of the Q4-U2 element. Only when the additional bubble terms are incorporated in the displacement and rotation function fields, which complete the full cubic linked interpolation form, qualitative improvement is fulfilled in the Q4-U3R5 element. Nevertheless, the locking problem exists even for the both presented elements, like in all pure displacement elements when applied to very thin plates modelled by coarse meshes. But good and even slightly better performance can be noticed for the Q4-U3R5 element when compared with elements from the literature, if the model meshes are moderately dense and the plate thickness not extremely thin. In some cases, it is comparable to or even better than Q9-U3 element which has as many as 12 more external degrees of freedom. A significant improvement can be noticed in particular when modeling very skew plates and models with singularities in the stress fields as well as circular plates with distorted meshes.

Keywords: Mindlin plate theory, problem-independent linked interpolation, problem-dependent interpolation, quadrilateral displacement-based plate finite elements

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Authors: Xiaoling Ren, Guidong Yang

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As one of the largest industrial synthetic chemicals in the world, ammonia has the advantages of high energy density, easy liquefaction, and easy transportation, which is widely used in agriculture, chemical industry, energy storage, and other fields. The industrial Haber-Bosch method process for ammonia synthesis is generally conducted under severe conditions. It is essential to develop a green, sustainable strategy for ammonia production to meet the growing demand. In this direction, photocatalytic nitrogen reduction has huge advantages over the traditional, well-established Haber-Bosch process, such as the utilization of natural sun light as the energy source and significantly lower pressure and temperature to affect the reaction process. However, the high activation energy of nitrogen and the low efficiency of photo-generated electron-hole separation in the photocatalyst result in low ammonia production yield. Many researchers focus on improving the catalyst. In addition to modifying the catalyst, improving the dispersion of the catalyst and making full use of active sites are also means to improve the overall catalytic activity. Few studies have been carried out on this, which is the aim of this work. In this work, by making full use of the nitrogen activation ability of WO3-x with defective sites, small size WO3-x photocatalyst with high dispersibility was constructed, while the growth of WO3-x was restricted by using a high specific surface area mesoporous SBA-15 molecular sieve with the regular pore structure as a template. The morphology of pure SBA-15 and WO3-x/SBA-15 was characterized byscanning electron microscopy (SEM). Compared with pure SBA-15, some small particles can be found in the WO3-x/SBA-15 material, which means that WO3-x grows into small particles under the limitation of SBA-15, which is conducive to the exposure of catalytically active sites. To elucidate the chemical nature of the material, the X-ray diffraction (XRD) analysis was conducted. The observed diffraction pattern inWO3-xis in good agreement with that of the JCPDS file no.71-2450. Compared with WO3-x, no new peaks appeared in WO3-x/SBA-15.It can be concluded that WO3-x/SBA-15 was synthesized successfully. In order to provide more active sites, the mass content of WO3-x was optimized. Then the photocatalytic nitrogen reduction performances of above samples were performed with methanol as a hole scavenger. The results show that the overall ammonia production performance of WO3-x/SBA-15 is improved than pure bulk WO3-x. The above results prove that making full use of active sites is also a means to improve overall catalytic activity.This work provides material basis for the design of high-efficiency photocatalytic nitrogen reduction catalysts.

Keywords: ammonia, photocatalytic, nitrogen reduction, WO3-x, high dispersibility

Procedia PDF Downloads 157

Authors: Ming-Chien Su, Yi-Zih Chen, Nien-Hsin Kao, Hideaki Shibata

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The major component of the atmosphere is nitrogen, yet atmospheric nitrogen has limited availability for biological use. Human activities have produced different types of nitrogen related compounds such as nitrogen oxides from combustion, nitrogen fertilizers from farming, and the nitrogen compounds from waste and wastewater, all of which have impacted the environment. Many studies have indicated the N-footprint is dominated by food, followed by housing, transportation, and goods and services sectors. To solve the impact issues from agricultural land, nitrogen cycle research is one of the key solutions. The study site is located in Hualien County, Taiwan, a major rice and food production area of Taiwan. Importantly, environmentally friendly farming has been promoted for years, and an environmental indicator system has been established by previous authors based on the concept of resilience capacity index (RCI) and environmental performance index (EPI). Nitrogen management is required for food production, as excess N causes environmental pollution. Therefore it is very important to develop a roadmap of the nitrogen footprint, and to integrate it with environmental indicators. The key focus of the study thus addresses (1) understanding the environmental impact caused by the nitrogen cycle of food products and (2) uncovering the trend of the N-footprint of agricultural products in Hualien, Taiwan. The N-footprint model was applied, which included both crops and energy consumption in the area. All data were adapted from government statistics databases and crosschecked for consistency before modeling. The actions involved with agricultural production were evaluated and analyzed for nitrogen loss to the environment, as well as measuring the impacts to humans and the environment. The results showed that rice makes up the largest share of agricultural production by weight, at 80%. The dominant meat production is pork (52%) and poultry (40%); fish and seafood were at similar levels to pork production. The average per capita food consumption in Taiwan is 2643.38 kcal capita−1 d−1, primarily from rice (430.58 kcal), meats (184.93 kcal) and wheat (ca. 356.44 kcal). The average protein uptake is 87.34 g capita−1 d−1, and 51% is mainly from meat, milk, and eggs. The preliminary results showed that the nitrogen footprint of food production is 34 kg N per capita per year, congruent with the results of Shibata et al. (2014) for Japan. These results provide a better understanding of the nitrogen demand and loss in the environment, and the roadmap can furthermore support the establishment of nitrogen policy and strategy. Additionally, the results serve to develop a roadmap of the nitrogen cycle of an environmentally friendly farming area, thus illuminating the nitrogen demand and loss of such areas.

Keywords: agriculture productions, energy consumption, environmental indicator, nitrogen footprint

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Authors: Abderrazak Bannari, Ghadeer Kadhem

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The objectives of this paper are the validation and the evaluation of MBES-CARIS BASE surface data performance for bathymetric mapping of shallow water in the Kingdom of Bahrain. The latter is an archipelago with a total land area of about 765.30 km², approximately 126 km of coastline and 8,000 km² of marine area, located in the Arabian Gulf, east of Saudi Arabia and west of Qatar (26° 00’ N, 50° 33’ E). To achieve our objectives, bathymetric attributed grid files (X, Y, and depth) generated from the coverage of ship-track MBSE data with 300 x 300 m cells, processed with CARIS-HIPS, were downloaded from the General Bathymetric Chart of the Oceans (GEBCO). Then, brought into ArcGIS and converted into a raster format following five steps: Exportation of GEBCO BASE surface data to the ASCII file; conversion of ASCII file to a points shape file; extraction of the area points covering the water boundary of the Kingdom of Bahrain and multiplying the depth values by -1 to get the negative values. Then, the simple Kriging method was used in ArcMap environment to generate a new raster bathymetric grid surface of 30×30 m cells, which was the basis of the subsequent analysis. Finally, for validation purposes, 2200 bathymetric points were extracted from a medium scale nautical map (1:100 000) considering different depths over the Bahrain national water boundary. The nautical map was scanned, georeferenced and overlaid on the MBES-CARIS generated raster bathymetric grid surface (step 5 above), and then homologous depth points were selected. Statistical analysis, expressed as a linear error at the 95% confidence level, showed a strong correlation coefficient (R² = 0.96) and a low RMSE (± 0.57 m) between the nautical map and derived MBSE-CARIS depths if we consider only the shallow areas with depths of less than 10 m (about 800 validation points). When we consider only deeper areas (> 10 m) the correlation coefficient is equal to 0.73 and the RMSE is equal to ± 2.43 m while if we consider the totality of 2200 validation points including all depths, the correlation coefficient is still significant (R² = 0.81) with satisfactory RMSE (± 1.57 m). Certainly, this significant variation can be caused by the MBSE that did not completely cover the bottom in several of the deeper pockmarks because of the rapid change in depth. In addition, steep slopes and the rough seafloor probably affect the acquired MBSE raw data. In addition, the interpolation of missed area values between MBSE acquisition swaths-lines (ship-tracked sounding data) may not reflect the true depths of these missed areas. However, globally the results of the MBES-CARIS data are very appropriate for bathymetric mapping of shallow water areas.

Keywords: bathymetry mapping, multibeam echosounder systems, CARIS-HIPS, shallow water

Procedia PDF Downloads 377

Authors: Fatemah Al-Baroud, Sudharani Shreenivas Kshatriya

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Kuwait Oil Company (KOC) is one of the companies for gas & oil production in Kuwait. The oil and gas industry is truly global; with operations conducted in every corner of the globe, the global community will rely heavily on oil and gas supplies. KOC has made many commitments to protect all due to KOC’s operations and operational releases. As per KOC’s strategy, the substantial increase in production activities will bring many challenges in managing various environmental hazards and stresses in the company. In order to handle those environmental challenges, the need of implementing effectively the health, safety, and environmental management system (HSEMS) is significant. And by implementing the HSEMS system properly, the environmental aspects of the activities, products, and services were identified, evaluated, and controlled in order to (i) Comply with local regulatory and other obligatory requirements; (ii) Comply with company policy and business requirements; and (iii) Reduce adverse environmental impact, including adverse impact to company reputation. Assessments for the Jurassic Production Facilities are being carried out as a part of the KOC HSEMS procedural requirement and monitoring the implementation of the relevant HSEMS procedures in the facilities. The assessments have been done by conducting series of theme audits using KOC’s audit protocol at JPFs. The objectives of the audits are to evaluate the compliance of the facilities towards the implementation of environmental procedures and the status of the KEPA requirement at all JPFs. The list of the facilities that were covered during the theme audit program are the following: (1) Jurassic Production Facility (JPF) – Sabriya (2) Jurassic Production Facility (JPF) – East Raudhatian (3) Jurassic Production Facility (JPF) – West Raudhatian (4)Early Production Facility (EPF 50). The auditing process comprehensively focuses on the application of KOC HSE MS procedures at JPFs and their ability to reduce the resultant negative impacts on the environment from the operations. Number of findings and observations were noted and highlighted in the audit reports and sent to all concerned controlling teams. The results of these audits indicated that the facilities, in general view, were in line with KOC HSE Procedures, and there were commitments in documenting all the HSE issues in the right records and plans. Further, implemented several control measures at JPFs that minimized/reduced the environmental impact, such as SRU were installed for sulphur recovery. Future scope and monitoring audit after a sufficient period of time will be carried out in conjunction with the controlling teams in order to verify the current status of the recommendations and evaluate the contractors' performance towards the required actions in preserving the environment.

Keywords: assessment of the environmental compliance, environmental and social impact assessment, kuwait environment public authority regulations, health, safety and environment management procedures, jurassic production facilities

Procedia PDF Downloads 184

Authors: Hassiba Benguergoura, Kamal Chanane, Sâad Moulay

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Pervaporation (PV), a membrane-based separation technology, has gained much attention because of its energy saving capability and low-cost, especially for separation of azeotropic or close-boiling liquid mixtures. There are two crucial issues for industrial application of pervaporation process. The first is developing membrane material and tailoring membrane structure to obtain high pervaporation performances. The second is modeling pervaporation transport to better understand of the above-mentioned structure–pervaporation relationship. Many models were proposed to predict the mass transfer process, among them, solution-diffusion model is most widely used in describing pervaporation transport including preferential sorption, diffusion and evaporation steps. For modeling pervaporation transport, the permeation flux, which depends on the solubility and diffusivity of components in the membrane, should be obtained first. Traditionally, the solubility was calculated according to the Flory–Huggins theory. Separation of the benzene (Bz)/cyclohexane (Cx) mixture is industrially significant. Numerous papers have been focused on the Bz/Cx system to assess the PV properties of membrane materials. Membranes with both high permeability and selectivity are desirable for practical application. Several new polymers have been prepared to get both high permeability and selectivity. Styrene-butadiene rubbers (SBR), dense membranes cross-linked by chloromethylation were used in the separation of benzene/cyclohexane mixtures. The impact of chloromethylation reaction as a new method of cross-linking SBR on the pervaporation performance have been reported. In contrast to the vulcanization with sulfur, the cross-linking takes places on styrene units of polymeric chains via a methylene bridge. The partial pervaporative (PV) fluxes of benzene/cyclohexane mixtures in styrene-butadiene rubber (SBR) were predicted using Fick's first law. The predicted partial fluxes and the PV separation factor agreed well with the experimental data by integrating Fick's law over the benzene concentration. The effects of feed concentration and operating temperature on the predicted permeation flux by this proposed model are investigated. The predicted permeation fluxes are in good agreement with experimental data at lower benzene concentration in feed, but at higher benzene concentration, the model overestimated permeation flux. The predicted and experimental permeation fluxes all increase with operating temperature increasing. Solvent sorption levels for benzene/ cyclohexane mixtures in a SBR membrane were determined experimentally. The results showed that the solvent sorption levels were strongly affected by the feed composition. The Flory- Huggins equation generates higher R-square coefficient for the sorption selectivity.

Keywords: benzene, cyclohexane, pervaporation, permeation, sorption modeling, SBR

Procedia PDF Downloads 325

Authors: Wisam H. Benamer, Ehab A. Elfallah, Mohamed A. Elshaari, Farag A. Elshaari

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A challenge for laboratories is to provide bacterial identification and antibiotic sensitivity results within a short time. Hence, advancement in the required technology is desirable to improve timing, accuracy and quality. Even with the current advances in methods used for both phenotypic and genotypic identification of bacteria the need is there to develop method(s) that enhance the outcome of bacteriology laboratories in accuracy and time. The hypothesis introduced here is based on the assumption that the chromosome of any bacteria contains unique sequences that can be used for its identification and typing. The outcome of a pilot study designed to test this hypothesis is reported in this manuscript. Methods: The complete chromosome sequences of several bacterial species were downloaded to use as search targets for unique sequences. Visual basic and SQL server (2014) were used to generate a complete set of 18-base long primers, a process started with reverse translation of randomly chosen 6 amino acids to limit the number of the generated primers. In addition, the software used to scan the downloaded chromosomes using the generated primers for similarities was designed, and the resulting hits were classified according to the number of similar chromosomal sequences, i.e., unique or otherwise. Results: All primers that had identical/similar sequences in the selected genome sequence(s) were classified according to the number of hits in the chromosomes search. Those that were identical to a single site on a single bacterial chromosome were referred to as unique. On the other hand, most generated primers sequences were identical to multiple sites on a single or multiple chromosomes. Following scanning, the generated primers were classified based on ability to differentiate between medically important bacterial and the initial results looks promising. Conclusion: A simple strategy that started by generating primers was introduced; the primers were used to screen bacterial genomes for match. Primer(s) that were uniquely identical to specific DNA sequence on a specific bacterial chromosome were selected. The identified unique sequence can be used in different molecular diagnostic techniques, possibly to identify bacteria. In addition, a single primer that can identify multiple sites in a single chromosome can be exploited for region or genome identification. Although genomes sequences draft of isolates of organism DNA enable high throughput primer design using alignment strategy, and this enhances diagnostic performance in comparison to traditional molecular assays. In this method the generated primers can be used to identify an organism before the draft sequence is completed. In addition, the generated primers can be used to build a bank for easy access of the primers that can be used to identify bacteria.

Keywords: bacteria chromosome, bacterial identification, sequence, primer generation

Procedia PDF Downloads 191

Authors: T. Lazzarini, M. Imbiki, P. E. Sutter, G. Borragan

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While several strategies, such as the development of more efficient appliances, the financing of insulation programs or the rolling out of smart meters represent promising tools to reduce future energy consumption, their implementation relies on people’s decisions-actions. Likewise, engaging with consumers to reshape their behavior has shown to be another important way to reduce energy usage. For these reasons, integrating the human factor in the energy transition has become a major objective for researchers and policymakers. Digital education programs based on tangible and gamified user interfaces have become a new tool with potential effects to reduce energy consumption4. The B2020 program, developed by the firm “Économie d’Énergie SAS”, proposes a digital platform to encourage pro-environmental behavior change among employees and citizens. The platform integrates 160 eco-behaviors to help saving energy and water and reducing waste and CO2 emissions. A total of 13,146 citizens have used the tool so far to declare the range of eco-behaviors they adopt in their daily lives. The present work seeks to build on this database to identify the potential impact of adopted energy-saving behaviors (n=62) to reduce the use of energy in buildings. To this end, behaviors were classified into three categories regarding the nature of its implementation (Eco-habits: e.g., turning-off the light, Eco-actions: e.g., installing low carbon technology such as led light-bulbs and Home-Refurbishments: e.g., such as wall-insulation or double-glazed energy efficient windows). General Linear Models (GLM) disclosed the existence of a significantly higher frequency of Eco-habits when compared to the number of home-refurbishments realized by the platform users. While this might be explained in part by the high financial costs that are associated with home renovation works, it also contrasts with the up to three times larger energy-savings that can be accomplished by these means. Furthermore, multiple regression models failed to disclose the expected relationship between energy-savings and frequency of adopted eco behaviors, suggesting that energy-related practices are not necessarily driven by the correspondent energy-savings. Finally, our results also suggested that people adopting more Eco-habits and Eco-actions were more likely to engage in Home-Refurbishments. Altogether, these results fit well with a growing body of scientific research, showing that energy-related practices do not necessarily maximize utility, as postulated by traditional economic models, and suggest that other variables might be triggering them. Promoting home refurbishments could benefit from the adoption of complementary energy-saving habits and actions.

Keywords: energy-saving behavior, human performance, behavioral change, energy efficiency

Procedia PDF Downloads 198

Authors: A. Alyaaria, b, J. P. Wignerona, A. Ducharneb, Y. Kerrc, P. de Rosnayd, R. de Jeue, A. Govinda, A. Al Bitarc, C. Albergeld, J. Sabaterd, C. Moisya, P. Richaumec, A. Mialonc

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Global Level-3 surface soil moisture (SSM) maps from the passive microwave soil moisture and Ocean Salinity satellite (SMOSL3) have been released. To further improve the Level-3 retrieval algorithm, evaluation of the accuracy of the spatio-temporal variability of the SMOS Level 3 products (referred to here as SMOSL3) is necessary. In this study, a comparative analysis of SMOSL3 with a SSM product derived from the observations of the Advanced Microwave Scanning Radiometer (AMSR-E) computed by implementing the Land Parameter Retrieval Model (LPRM) algorithm, referred to here as AMSRM, is presented. The comparison of both products (SMSL3 and AMSRM) were made against SSM products produced by a numerical weather prediction system (SM-DAS-2) at ECMWF (European Centre for Medium-Range Weather Forecasts) for the 03/2010-09/2011 period at global scale. The latter product was considered here a 'reference' product for the inter-comparison of the SMOSL3 and AMSRM products. Three statistical criteria were used for the evaluation, the correlation coefficient (R), the root-mean-squared difference (RMSD), and the bias. Global maps of these criteria were computed, taking into account vegetation information in terms of biome types and Leaf Area Index (LAI). We found that both the SMOSL3 and AMSRM products captured well the spatio-temporal variability of the SM-DAS-2 SSM products in most of the biomes. In general, the AMSRM products overestimated (i.e., wet bias) while the SMOSL3 products underestimated (i.e., dry bias) SSM in comparison to the SM-DAS-2 SSM products. In term of correlation values, the SMOSL3 products were found to better capture the SSM temporal dynamics in highly vegetated biomes ('Tropical humid', 'Temperate Humid', etc.) while best results for AMSRM were obtained over arid and semi-arid biomes ('Desert temperate', 'Desert tropical', etc.). When removing the seasonal cycles in the SSM time variations to compute anomaly values, better correlation with the SM-DAS-2 SSM anomalies were obtained with SMOSL3 than with AMSRM, in most of the biomes with the exception of desert regions. Eventually, we showed that the accuracy of the remotely sensed SSM products is strongly related to LAI. Both the SMOSL3 and AMSRM (slightly better) SSM products correlate well with the SM-DAS2 products over regions with sparse vegetation for values of LAI < 1 (these regions represent almost 50% of the pixels considered in this global study). In regions where LAI>1, SMOSL3 outperformed AMSRM with respect to SM-DAS-2: SMOSL3 had almost consistent performances up to LAI = 6, whereas AMSRM performance deteriorated rapidly with increasing values of LAI.

Keywords: remote sensing, microwave, soil moisture, AMSR-E, SMOS

Procedia PDF Downloads 356

Authors: Taher Abofol, Yaakov Kareev, Judith Avrahami, Peter M. Todd

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Are children sensitive to their relative strength in competitions against others? Performance on tasks that require cooperation or coordination (e.g. the Ultimatum Game) indicates that early precursors of adult-like notions of fairness and reciprocity, as well as altruistic behavior, are evident at an early age. However, not much is known regarding developmental changes in interactive decision-making, especially in competitive interactions. Thus, it is important to study the developmental aspects of strategic behavior in these situations. The present research focused on cognitive-developmental changes in a competitive interaction. Specifically, it aimed at revealing how children engage in strategic interactions that involve the allocation of limited resources over a number of fields of competition, by manipulating relative strength. Relative strength refers to situations in which player strength changes midway through the game: the stronger player becomes the weaker one, while the weaker player becomes the stronger one. An experiment was conducted to find out if the behavior of children of different age groups differs in the following three aspects: 1. Perception of relative strength. 2. Ability to learn while gaining experience. 3. Ability to adapt to change in relative strength. The task was composed of a resource allocation game. After the players allocated their resources (privately and simultaneously), a competition field was randomly chosen for each player. The player who allocated more resources to the field chosen was declared the winner of that round. The resources available to the two competitors were unequal (or equal, for control). The theoretical solution for this game is that the weaker player should give up on a certain number of fields, depending on the stronger opponent’s relative strength, in order to be able to compete with the opponent on equal footing in the remaining fields. Participants were of three age groups, first-graders (N = 36, mean age = 6), fourth-graders (N = 36, mean age = 10), and eleventh-graders (N = 72, mean age = 16). The games took place between players of the same age and lasted for 16 rounds. There were two experimental conditions – a control condition, in which players were of equal strength, and an experimental condition, in which players differed in strength. In the experimental condition, players' strength was changed midway through the session. Results indicated that players in all age groups were sensitive to their relative strength, and played in line with the theoretical solution: the weaker players gave up on more fields than the stronger ones. This understanding, as well as the consequent difference in allocation between weak and strong players, was more pronounced among older participants. Experience led only to minimal behavioral change. Finally, the children from the two older groups, particularly the eleventh graders adapted quickly to the midway switch in relative strength. In contrast, the first-graders hardly changed their behavior with the change in their relative strength, indicating a limited ability to adapt. These findings highlight young children’s ability to consider their relative strength in strategic interactions and its boundaries.

Keywords: children, competition, decision making, developmental changes, strategic behavior

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Authors: E. M. Condori-Peñaloza, S. S. Costa

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Eleusine indica L, known as goose-grass, is considered a troublesome weed that can cause important economic losses in the agriculture worldwide. However, this grass is used as a medicinal plant in some regions of Brazil to treat influenza and pneumonia. In Africa and Asia, it is used to treat malaria and as diuretic, anti-helminthic, among other uses. Despite its therapeutic potential, little is known about the chemical composition and bioactive compounds of E. indica. Hitherto, two major flavonoids, schaftoside and vitexin, were isolated from aerial part of the species and showed inhibitory activity on lung neutrophil influxes in mice, suggesting a beneficial effect on airway inflammation. Therefore, the aim of this study was to analyze the chemical profile of aqueous extracts from aerial parts of Eleusine indica specimens using high performance liquid chromatography (HPLC-DAD) and 1H nuclear magnetic resonance spectroscopy (NMR), with emphasis on phenolic compounds. Specimens of E. indica were collected in Minas Gerais state, Brazil. Aerial parts of fresh plants were extracted by decoction (10% p/v). After spontaneous precipitation of the aqueous extract at 10-12°C for 24 hours, the supernatant obtained was frozen and lyophilized. After that, 1 g of the extract was dissolved into 25 mL of water and fractionated on a reverse phase chromatography column (RP-2), eluted with a gradient of H2O/EtOH. Five fractions were obtained. The extract and fractions had their chemical profile analyzed by using HPLC-DAD (C-18 column: 20 μL, 256 -365 nm; gradient water 0.01% phosphoric acid/ acetonitrile. The extract was also analyzed by NMR (1H, 500 MHz, D2O) in order to access its global chemical composition. HPLC-DAD analyses of crude extract allowed the identification of ten phenolic compounds. Fraction 1, eluted with 100% water, was poor in phenolic compounds and no major peak was detected. In fraction 2, eluted with 100% water, it was possible to observe one major peak at retention time (RT) of 23.75 minutes compatible with flavonoid; fraction 3, also eluted with 100% water, showed four peaks at RT= 21.47, 23.52, 24.33 and 25.84 minutes, all of them compatible with flavonoid. In fraction 4, eluted with 50%/ethanol/50% water, it was possible to observe 3 peaks compatible with flavonoids at RT=24.65, 26.81, 27.49 minutes, and one peak (28.83 min) compatible with a phenolic acid derivative. Finally, in fraction 5, eluted with 100% ethanol, no phenolic substance was detected. The UV spectra of all flavonoids detected were compatible with the flavone subclass (λ= 320-345 nm). The 1H NMR spectra of aerial parts extract showed signals in three regions: δ 0.8-3.0 ppm (aliphatic compounds), δ 3.0-5.5 ppm corresponding to carbohydrates (signals most abundant and overlapped), and δ 6.0-8.5 ppm (aromatic compounds). Signals compatible with flavonoids (rings A and B) could also be detected in the crude extract spectra. These results suggest the presence of several flavonoids in E. indica, which reinforces their therapeutic potential. The pharmacological activities of Eleusine indica extracts and fractions will be further evaluated.

Keywords: flavonoids, HPLC, NMR, phenolic compounds

Procedia PDF Downloads 317

Authors: Chii-Dong Ho, Jian-Har Chen

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The decline in permeate flux across membrane modules is attributed to the increase in temperature polarization resistance in flat-plate Direct Contact Membrane Distillation (DCMD) modules for pure water productivity. Researchers have discovered that this effect can be diminished by embedding turbulence promoters, which augment turbulence intensity at the cost of increased power consumption, thereby improving vapor permeate flux. The device performance of DCMD modules for permeate flux was further enhanced by shrinking the hydraulic diameters of inserted S-ribs carbon-fiber promoters as well as considering the energy consumption increment. The mass-balance formulation, based on the resistance-in-series model by energy conservation in one-dimensional governing equations, was developed theoretically and conducted experimentally on a flat-plate polytetrafluoroethylene/polypropylene (PTFE/PP) membrane module to predict permeate flux and temperature distributions. The ratio of permeate flux enhancement to energy consumption increment, as referred to an assessment on economic viewpoint and technical feasibilities, was calculated to determine the suitable design parameters for DCMD operations with the insertion of S-ribs carbon-fiber turbulence promoters. An economic analysis was also performed, weighing both permeate flux improvement and energy consumption increment on modules with promoter-filled channels by different array configurations and various hydraulic diameters of turbulence promoters. Results showed that the ratio of permeate flux improvement to energy consumption increment in descending hydraulic-diameter modules is higher than in uniform hydraulic-diameter modules. The fabrication details of the DCMD module filaments implementing the S-ribs carbon-fiber filaments and the schematic configuration of the flat-plate DCMD experimental setup with presenting acrylic plates as external walls were demonstrated in the present study. The S-ribs carbon fibers perform as turbulence promoters incorporated into the artificial hot saline feed stream, which was prepared by adding inorganic salts (NaCl) to distilled water. Theoretical predictions and experimental results exhibited a great accomplishment to considerably achieve permeate flux enhancement, such as the new design of the DCMD module with inserting S-ribs carbon-fiber promoters. Additionally, the Nusselt number for the water vapor transferring membrane module with inserted S-ribs carbon-fiber promoters was generalized into a simplified expression to predict the heat transfer coefficient and permeate flux as well.

Keywords: permeate flux, Nusselt number, DCMD module, temperature polarization, hydraulic diameters

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Authors: Akinola Helen Olufunke, Soetan Olatunbosun Jonathan, Adeleye Oludamola

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Introduction: Antibiotic resistance has grown significantly, which is a major cause for concern. There have not been many significant developments in antibiotics over the past few decades, and practically all of the ones that are currently in use are losing effectiveness against pathogenic germs. Researchers are starting to focus more on the physiologically active compounds found in plants, particularly phytobiotics in poultry production. Consumption of chicken products is among the greatest in the country, but numerous nations, including Nigeria, use excessive amounts of necessary antibiotics in poultry farming, endangering the safety of such goods (through antimicrobial residues). Drug resistance has become a widespread issue as a result of the risky use of antibiotics in the chicken production industry. In order to replace antibiotics, biotic or natural products like phytobiotics (also known as botanicals or phytogenics) have drawn a lot of interest. Phytobiotics or their components are thought to be a relatively recent category of natural herbs that have acquired acceptance and favor among chicken farmers. The addition of several phytobiotic additions to poultry feed has demonstrated its capacity to improve both the broiler and layer populations' productivity. Design: Experimental research design and cross-sectional study was carried out at every 300 purposively selected farming household in the six-geopolitical zone in Nigeria. Data Analysis: A semi-structured questionnaire was administered to each farmer, and quantitative data were analyzed using Statistical Package for Social Science (SPSS) while the Chi-square test was used to analyze factors mitigating the use of Phytobiotics. Result: The result shows that the benefits associated with the use of phytobiotics are contributed to growth promotion in chickens and enhancement of productive performance of broiler and layer, which could be attributed to their antioxidant activity. The result further revealed that factors mitigating the use of phytobiotics were lack of knowledge in the use of phytobiotics, overdose or underdose usage, and seasonal availability of the phytobiotics. Others are the educational level of the farmers, intrinsic motivation, income poultry farming experience, price of phytobiotics based additives feeds, and intensity of extension agents in visiting them. Conclusion: The difficulties associated with using phytobiotics in chicken farms limit their willingness to boost productivity. The study found that most farmers were ignorant, which prevented them from handling this notion and turning their poultry into a viable enterprise while also allowing them to be creative. They believed that packing phytobiotics-based additive feed was expensive, and lastly, the seasonal availability of some phytobiotics. Recommendation: Further research in phytobiotics use in Nigeria should be carried out in order to establish its efficiency, safety, and awareness.

Keywords: mitigating, antibiotics, phytobiotics, poultry farming

Procedia PDF Downloads 170

Authors: Abid Hussain, Binay Kumar

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Environment-friendly lead-free Na₀.₅₂K₀.₄₄Li₀.₀₄Nb₀.₈₄Ta₀.₁₀Sb₀.₀₆O₃ (NKLNTS) ceramic was synthesized by solid-state reaction method in search of a potential candidate to replace lead-based ceramics such as PbZrO₃-PbTiO₃ (PZT), Pb(Mg₁/₃Nb₂/₃)O₃-PbTiO₃ (PMN-PT) etc., for various applications. The ceramic was calcined at temperature 850 ᵒC and sintered at 1090 ᵒC. The powder X-Ray Diffraction (XRD) pattern revealed the formation of pure perovskite phase having tetragonal symmetry with space group P4mm of the synthesized ceramic. The surface morphology of the ceramic was studied using Field Emission Scanning Electron Microscopy (FESEM) technique. The well-defined grains with homogeneous microstructure were observed. The average grain size was found to be ~ 0.6 µm. A very large value of piezoelectric charge coefficient (d₃₃ ~ 754 pm/V) was obtained for the synthesized ceramic which indicated its potential for use in transducers and actuators. In dielectric measurements, a high value of ferroelectric to paraelectric phase transition temperature (Tm~305 ᵒC), a high value of maximum dielectric permittivity ~ 2110 (at 1 kHz) and a very small value of dielectric loss ( < 0.6) were obtained which suggested the utility of NKLNTS ceramic in high-temperature ferroelectric devices. Also, the degree of diffuseness (γ) was found to be 1.61 which confirmed a relaxor ferroelectric behavior in NKLNTS ceramic. P-E hysteresis loop was traced and the value of spontaneous polarization was found to be ~11μC/cm² at room temperature. The pyroelectric coefficient was obtained to be very high (p ∼ 1870 μCm⁻² ᵒC⁻¹) for the present case indicating its applicability in pyroelectric detector applications including fire and burglar alarms, infrared imaging, etc. NKLNTS ceramic showed fatigue free behavior over 107 switching cycles. Remanent hysteresis task was performed to determine the true-remanent (or intrinsic) polarization of NKLNTS ceramic by eliminating non-switchable components which showed that a major portion (83.10 %) of the remanent polarization (Pr) is switchable in the sample which makes NKLNTS ceramic a suitable material for memory switching devices applications. Time-Dependent Compensated (TDC) hysteresis task was carried out which revealed resistive leakage free nature of the ceramic. The performance of NKLNTS ceramic was found to be superior to many lead based piezoceramics and hence can effectively replace them for use in piezoelectric, pyroelectric and long duration ferroelectric applications.

Keywords: dielectric properties, ferroelectric properties , lead free ceramic, piezoelectric property, solid state reaction, true-remanent polarization

Procedia PDF Downloads 134

Authors: Ravija Gunawardana, Banuka Athuraliya

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Machine learning has emerged as a powerful tool for disease diagnosis and prediction. The use of machine learning algorithms has the potential to improve the accuracy of disease prediction, thereby enabling medical professionals to provide more effective and personalized treatments. This study focuses on developing a machine-learning model for disease prediction using symptoms and X-ray images. The importance of this study lies in its potential to assist medical professionals in accurately diagnosing diseases, thereby improving patient outcomes. Respiratory diseases are a significant cause of morbidity and mortality worldwide, and chest X-rays are commonly used in the diagnosis of these diseases. However, accurately interpreting X-ray images requires significant expertise and can be time-consuming, making it difficult to diagnose respiratory diseases in a timely manner. By incorporating machine learning algorithms, we can significantly enhance disease prediction accuracy, ultimately leading to better patient care. The study utilized the Mask R-CNN algorithm, which is a state-of-the-art method for object detection and segmentation in images, to process chest X-ray images. The model was trained and tested on a large dataset of patient information, which included both symptom data and X-ray images. The performance of the model was evaluated using a range of metrics, including accuracy, precision, recall, and F1-score. The results showed that the model achieved an accuracy rate of over 90%, indicating that it was able to accurately detect and segment regions of interest in the X-ray images. In addition to X-ray images, the study also incorporated symptoms as input data for disease prediction. The study used three different classifiers, namely Random Forest, K-Nearest Neighbor and Support Vector Machine, to predict diseases based on symptoms. These classifiers were trained and tested using the same dataset of patient information as the X-ray model. The results showed promising accuracy rates for predicting diseases using symptoms, with the ensemble learning techniques significantly improving the accuracy of disease prediction. The study's findings indicate that the use of machine learning algorithms can significantly enhance disease prediction accuracy, ultimately leading to better patient care. The model developed in this study has the potential to assist medical professionals in diagnosing respiratory diseases more accurately and efficiently. However, it is important to note that the accuracy of the model can be affected by several factors, including the quality of the X-ray images, the size of the dataset used for training, and the complexity of the disease being diagnosed. In conclusion, the study demonstrated the potential of machine learning algorithms for disease prediction using symptoms and X-ray images. The use of these algorithms can improve the accuracy of disease diagnosis, ultimately leading to better patient care. Further research is needed to validate the model's accuracy and effectiveness in a clinical setting and to expand its application to other diseases.

Keywords: K-nearest neighbor, mask R-CNN, random forest, support vector machine

Procedia PDF Downloads 151

Authors: Anirudh Lahiri

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Neuromorphic computing, inspired by the intricate operations of biological neural networks, offers a revolutionary approach to overcoming the limitations of traditional computing architectures. This research proposes the integration of spintronics with neuromorphic systems, aiming to enhance computational performance, scalability, and energy efficiency. Traditional computing systems, based on the Von Neumann architecture, struggle with scalability and efficiency due to the segregation of memory and processing functions. In contrast, the human brain exemplifies high efficiency and adaptability, processing vast amounts of information with minimal energy consumption. This project explores the use of spintronics, which utilizes the electron's spin rather than its charge, to create more energy-efficient computing systems. Spintronic devices, such as magnetic tunnel junctions (MTJs) manipulated through spin-transfer torque (STT) and spin-orbit torque (SOT), offer a promising pathway to reducing power consumption and enhancing the speed of data processing. The integration of these devices within a neuromorphic framework aims to replicate the efficiency and adaptability of biological systems. The research is structured into three phases: an exhaustive literature review to build a theoretical foundation, laboratory experiments to test and optimize the theoretical models, and iterative refinements based on experimental results to finalize the system. The initial phase focuses on understanding the current state of neuromorphic and spintronic technologies. The second phase involves practical experimentation with spintronic devices and the development of neuromorphic systems that mimic synaptic plasticity and other biological processes. The final phase focuses on refining the systems based on feedback from the testing phase and preparing the findings for publication. The expected contributions of this research are twofold. Firstly, it aims to significantly reduce the energy consumption of computational systems while maintaining or increasing processing speed, addressing a critical need in the field of computing. Secondly, it seeks to enhance the learning capabilities of neuromorphic systems, allowing them to adapt more dynamically to changing environmental inputs, thus better mimicking the human brain's functionality. The integration of spintronics with neuromorphic computing could revolutionize how computational systems are designed, making them more efficient, faster, and more adaptable. This research aligns with the ongoing pursuit of energy-efficient and scalable computing solutions, marking a significant step forward in the field of computational technology.

Keywords: material science, biological engineering, mechanical engineering, neuromorphic computing, spintronics, energy efficiency, computational scalability, synaptic plasticity.

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Authors: Vinu Viswanath, Lawrence Dsouza, Ugo Ravon

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Syngas typically and intermediary gas product has a wide range of application of producing various chemical products, such as mixed alcohols, hydrogen, ammonia, Fischer-Tropsch products methanol, ethanol, aldehydes, alcohols, etc. There are several technologies available for the syngas production. An alternative to the conventional processes an attractive route of utilizing carbon dioxide and methane in equimolar ratio to generate syngas of ratio close to one has been developed which is also termed as Dry Reforming of Methane technology. It also gives the privilege to utilize the greenhouse gases like CO2 and CH4. The dry reforming process is highly endothermic, and indeed, ΔG becomes negative if the temperature is higher than 900K and practically, the reaction occurs at 1000-1100K. At this temperature, the sintering of the metal particle is happening that deactivate the catalyst. However, by using this strategy, the methane is just partially oxidized, and some cokes deposition occurs that causing the catalyst deactivation. The current research work was focused to mitigate the main challenges of dry reforming process such coke deposition, and metal sintering at high temperature.To achieve these objectives, we employed three different strategies of catalyst development. 1) Use of bulk catalysts such as olivine and pyrochlore type materials. 2) Use of metal doped support materials, like spinel and clay type material. 3) Use of core-shell model catalyst. In this approach, a thin layer (shell) of redox metal oxide is deposited over the MgAl2O4 /Al2O3 based support material (core). For the core-shell approach, an active metal is been deposited on the surface of the shell. The shell structure formed is a doped metal oxide that can undergo reduction and oxidation reactions (redox), and the core is an alkaline earth aluminate having a high affinity towards carbon dioxide. In the case of metal-doped support catalyst, the enhanced redox properties of doped CeO2 oxide and CO2 affinity property of alkaline earth aluminates collectively helps to overcome coke formation. For all of the mentioned three strategies, a systematic screening of the metals is carried out to optimize the efficiency of the catalyst. To evaluate the performance of them, the activity and stability test were carried out under reaction conditions of temperature ranging from 650 to 850 ̊C and an operating pressure ranging from 1 to 20 bar. The result generated infers that the core-shell model catalyst showed high activity and better stable DR catalysts under atmospheric as well as high-pressure conditions. In this presentation, we will show the results related to the strategy.

Keywords: carbon dioxide, dry reforming, supports, core shell catalyst

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Authors: Jianan Sun, Ziwen Ye

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Multidimensional computerized adaptive testing (MCAT) is a popular research topic in psychometrics. It is important for practitioners to clearly know the item-trait patterns of administered items when a test like MCAT is operated. Item-trait pattern recognition refers to detecting which latent traits in a psychological test are measured by each of the specified items. If the item-trait patterns of the replenished items in MCAT item pool are well detected, the interpretability of the items can be improved, which can further promote the abilities of the examinees who attending the MCAT to be accurately estimated. This research explores to solve the item-trait pattern recognition problem of the replenished items in MCAT item pool from the perspective of statistical variable selection. The popular multidimensional item response theory model, multidimensional two-parameter logistic model, is assumed to fit the response data of MCAT. The proposed method uses the least absolute shrinkage and selection operator (LASSO) to detect item-trait patterns of replenished items based on the essential information of item responses and ability estimates of examinees collected from a designed MCAT procedure. Several advantages of the proposed method are outlined. First, the proposed method does not strictly depend on the relative order between the replenished items and the selected operational items, so it allows the replenished items to be mixed into the operational items in reasonable order such as considering content constraints or other test requirements. Second, the LASSO used in this research improves the interpretability of the multidimensional replenished items in MCAT. Third, the proposed method can exert the advantage of shrinkage method idea for variable selection, so it can help to check item quality and key dimension features of replenished items and saves more costs of time and labors in response data collection than traditional factor analysis method. Moreover, the proposed method makes sure the dimensions of replenished items are recognized to be consistent with the dimensions of operational items in MCAT item pool. Simulation studies are conducted to investigate the performance of the proposed method under different conditions for varying dimensionality of item pool, latent trait correlation, item discrimination, test lengths and item selection criteria in MCAT. Results show that the proposed method can accurately detect the item-trait patterns of the replenished items in the two-dimensional and the three-dimensional item pool. Selecting enough operational items from the item pool consisting of high discriminating items by Bayesian A-optimality in MCAT can improve the recognition accuracy of item-trait patterns of replenished items for the proposed method. The pattern recognition accuracy for the conditions with correlated traits is better than those with independent traits especially for the item pool consisting of comparatively low discriminating items. To sum up, the proposed data-driven method based on the LASSO can accurately and efficiently detect the item-trait patterns of replenished items in MCAT.

Keywords: item-trait pattern recognition, least absolute shrinkage and selection operator, multidimensional computerized adaptive testing, variable selection

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Authors: Julien Benomar

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The World Bank report published in April 2023 called “Migrants, Refugees and Society” allows us to first distinguish migrants in search of economic opportunities and refugees that flee a situation of danger and choose their destination based on their immediate need for safety. Amongst those two categories, the report distinguished people having professional skills adapted to the labor market of the host country and those who have not. Out of that distinction of four categories, we choose to focus our research on refugees that do not have professional skills adapted to the labor market of the host country. Given that refugees generally have no recourse to public assistance schemes and cannot count on the support of their entourage or support network, we propose to examine the extent to which external assistance, such as international humanitarian action, is likely to accompany refugees' transition to financial empowerment through entrepreneurship. To this end, we propose to carry out a case study structured in three stages: (i) an exchange with a Non-Governmental Organisation (NGO) active in supporting refugee populations from Congo and Burundi to Rwanda, enabling us to (i.i) define together a financial empowerment income, and (i. ii) learn about the content of the support measures taken for the beneficiaries of the humanitarian project; (ii) monitor the population of 118 beneficiaries, including 73 refugees and 45 Rwandans (reference population); (iii) conduct a participatory analysis to identify the level of performance of the project and areas for improvement. The case study thus involved the staff of an international NGO active in helping refugees from Rwanda since 2015 and the staff of a Luxembourg NGO that has been funding this economic aid project through entrepreneurship since 2021. The case study thus involved the staff of an international NGO active in helping refugees from Rwanda since 2015 and the staff of a Luxembourg NGO, which has been funding this economic aid through an entrepreneurship project since 2021, and took place over a 48-day period between April and May 2023. The main results are of two types: (i) the need to associate indicators for monitoring the impact of the project on the indirect beneficiaries of the project (refugee community) and (ii) the identification of success factors making it possible to bring concrete and relevant responses to the constraints encountered. The first result thus made it possible to identify the following indicators: Indicator of community potential ((jobs, training or mentoring) promoted by the activity of the entrepreneur), Indicator of social contribution (tax paid by the entrepreneur), Indicator of resilience (savings and loan capacity generated, and finally impact on social cohesion. The second result made it possible to identify that among the 7 success factors tested, the sector of activity chosen and the level of experience in the sector of the future activity are those that stand out the most clearly.

Keywords: entrepreuneurship, refugees, financial empowerment, international aid

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Authors: Biao Sun, Tejas Bhatelia, Vishnu Pareek, Ranjeet Utikar, Moses Tadé

Abstract:

Additive manufacturing (AM), commonly known as 3D printing, with the continuing advances in parallel processing and computational modeling, has created a paradigm shift (with significant radical thinking) in the design and operation of chemical processing plants, especially LNG plants. With the rising energy demands, environmental pressures, and economic challenges, there is a continuing industrial need for disruptive technologies such as AM, which possess capabilities that can drastically reduce the cost of manufacturing and operations of chemical processing plants in the future. However, the continuing challenge for 3D printing is its lack of adaptability in re-designing the process plant equipment coupled with the non-existent theory or models that could assist in selecting the optimal candidates out of the countless potential fabrications that are possible using AM. One of the most common packings used in the LNG process is structured packing in the packed column (which is a unit operation) in the process. In this work, we present an example of an optimum strategy for the application of AM to this important unit operation. Packed columns use a packing material through which the gas phase passes and comes into contact with the liquid phase flowing over the packing, typically performing the necessary mass transfer to enrich the products, etc. Structured packing consists of stacks of corrugated sheets, typically inclined between 40-70° from the plane. Computational Fluid Dynamics (CFD) was used to test and model various geometries to study the governing hydrodynamic characteristics. The results demonstrate that the costly iterative experimental process can be minimized. Furthermore, they also improve the understanding of the fundamental physics of the system at the multiscale level. SpiroPak, patented by Curtin University, represents an innovative structured packing solution currently at a technology readiness level (TRL) of 5~6. This packing exhibits remarkable characteristics, offering a substantial increase in surface area while significantly enhancing hydrodynamic and mass transfer performance. Recent studies have revealed that SpiroPak can reduce pressure drop by 50~70% compared to commonly used commercial packings, and it can achieve 20~50% greater mass transfer efficiency (particularly in CO2 absorption applications). The implementation of SpiroPak has the potential to reduce the overall size of columns and decrease power consumption, resulting in cost savings for both capital expenditure (CAPEX) and operational expenditure (OPEX) when applied to retrofitting existing systems or incorporated into new processes. Furthermore, pilot to large-scale tests is currently underway to further advance and refine this technology.

Keywords: Additive Manufacturing (AM), 3D printing, Computational Fluid Dynamics (CFD, structured packing (SpiroPak)

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Authors: Jean Pierre Ribeiro Daquila

Abstract:

This paper aims to analyze the use of the Theory of Multiple Intelligences as a tool to facilitate students’ learning. This theory, proposed by the American psychologist and educator Howard Gardner, was first established in 1983 and advocates that human beings possess eight intelligence and not only one, as defended by psychologists prior to his theory. These intelligence are bodily-kinesthetic intelligence, musical, linguistic, logical-mathematical, spatial, interpersonal, intrapersonal, and naturalist. This paper will focus on bodily-kinesthetic intelligence. Spatial and bodily-kinesthetic intelligences are sensed by athletes, dancers, and others who use their bodies in ways that exceed normal abilities. These are intelligences that are closely related. A quarterback or a ballet dancer needs to have both an awareness of body motions and abilities as well as a sense of the space involved in the action. Nevertheless, there are many reasons which make classical ballet dance more integrated with other intelligences. Ballet dancers make it look effortless as they move across the stage, from the lifts to the toe points; therefore, there is acting both in the performance of the repertoire and in hiding the pain or physical stress. The ballet dancer has to have great mathematical intelligence to perform a fast allegro; for instance, each movement has to be executed in a specific millisecond. Flamenco dancers need to rely as well on their mathematic abilities, as the footwork requires the ability to make half, two, three, four or even six movements in just one beat. However, the precision of the arm movements is freer than in ballet dance; for this reason, ballet dancers need to be more holistically aware of their movements; therefore, our experiment will test whether this greater attention required by ballet dancers makes them acquire better results in the training sessions when compared to flamenco dancers. An experiment will be carried out in this study by training ballet dancers through dance (four years of experience dancing minimum – experimental group 1); a group of flamenco dancers (four years of experience dancing minimum – experimental group 2). Both experimental groups will be trained in two different domains – phonetics and chemistry – to examine whether there is a significant improvement in these areas compared to the control group (a group of regular students who will receive the same training through a traditional method). However, this paper will focus on phonetic training. Experimental group 1 will be trained with the aid of classical music plus bodily work. Experimental group 2 will be trained with flamenco rhythm and kinesthetic work. We would like to highlight that this study takes dance as an example of a possible area of strength; nonetheless, other types of arts can and should be used to support students, such as drama, creative writing, music and others. The main aim of this work is to suggest that other intelligences, in the case of this study, bodily-kinesthetic, can be used to help improve pronunciation.

Keywords: multiple intelligences, pronunciation, effective pronunciation trainings, short drills, musical intelligence, bodily-kinesthetic intelligence

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Authors: Lakshmidevi Sreeramareddy, Komalpreet Kaur, Nane Pothier

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Graphical-based passwords have existed for decades. Their major advantage is that they are easier to remember than an alphanumeric password. However, their disadvantage (especially recognition-based passwords) is the smaller password space, making them more vulnerable to brute force attacks. Graphical passwords are also highly susceptible to the shoulder-surfing effect. The gesture-based password method that we developed is a grid-free, template-free method. In this study, we evaluated the gesture-based passwords for usability and vulnerability. The results of the study are significant. We developed a gesture-based password application for data collection. Two modes of data collection were used: Creation mode and Replication mode. In creation mode (Session 1), users were asked to create six different passwords and reenter each password five times. In replication mode, users saw a password image created by some other user for a fixed duration of time. Three different duration timers, such as 5 seconds (Session 2), 10 seconds (Session 3), and 15 seconds (Session 4), were used to mimic the shoulder-surfing attack. After the timer expired, the password image was removed, and users were asked to replicate the password. There were 74, 57, 50, and 44 users participated in Session 1, Session 2, Session 3, and Session 4 respectfully. In this study, the machine learning algorithms have been applied to determine whether the person is a genuine user or an imposter based on the password entered. Five different machine learning algorithms were deployed to compare the performance in user authentication: namely, Decision Trees, Linear Discriminant Analysis, Naive Bayes Classifier, Support Vector Machines (SVMs) with Gaussian Radial Basis Kernel function, and K-Nearest Neighbor. Gesture-based password features vary from one entry to the next. It is difficult to distinguish between a creator and an intruder for authentication. For each password entered by the user, four features were extracted: password score, password length, password speed, and password size. All four features were normalized before being fed to a classifier. Three different classifiers were trained using data from all four sessions. Classifiers A, B, and C were trained and tested using data from the password creation session and the password replication with a timer of 5 seconds, 10 seconds, and 15 seconds, respectively. The classification accuracies for Classifier A using five ML algorithms are 72.5%, 71.3%, 71.9%, 74.4%, and 72.9%, respectively. The classification accuracies for Classifier B using five ML algorithms are 69.7%, 67.9%, 70.2%, 73.8%, and 71.2%, respectively. The classification accuracies for Classifier C using five ML algorithms are 68.1%, 64.9%, 68.4%, 71.5%, and 69.8%, respectively. SVMs with Gaussian Radial Basis Kernel outperform other ML algorithms for gesture-based password authentication. Results confirm that the shorter the duration of the shoulder-surfing attack, the higher the authentication accuracy. In conclusion, behavioral features extracted from the gesture-based passwords lead to less vulnerable user authentication.

Keywords: authentication, gesture-based passwords, machine learning algorithms, shoulder-surfing attacks, usability

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Authors: Claire Tranter, Coral Dando

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Gathering information from others is a fundamental goal for those concerned with investigating crime, and protecting national and international security. Persuading an individual to move from an opposing to converging viewpoint, and an understanding on the cognitive style behind this change can serve to increase understanding of traditional face-to-face interactions, as well as synthetic environments (SEs) often used for communication across varying geographical locations. SEs are growing in usage, and with this increase comes an increase in crime being undertaken online. Communication technologies can allow people to mask their real identities, supporting anonymous communication which can raise significant challenges for investigators when monitoring and managing these conversations inside SEs. To date, the psychological literature concerning how to maximise information-gain in SEs for real-world interviewing purposes is sparse, and as such this aspect of social cognition is not well understood. Here, we introduce an overview of a novel programme of PhD research which seeks to enhance understanding of cross-cultural and cross-gender communication in SEs for maximising information gain. Utilising a dyadic jury paradigm, participants interacted with a confederate who attempted to persuade them to the opposing verdict across three distinct environments: face-to-face, instant messaging, and a novel virtual reality environment utilising avatars. Participants discussed a criminal scenario, acting as a two-person (male; female) jury. Persuasion was manipulated by the confederate claiming an opposing viewpoint (guilty v. not guilty) to the naïve participants from the outset. Pre and post discussion data, and observational digital recordings (voice and video) of participant’ discussion performance was collected. Information regarding cognitive style was also collected to ascertain participants need for cognitive closure and biases towards jumping to conclusions. Findings revealed that individuals communicating via an avatar in a virtual reality environment reacted in a similar way, and thus equally persuasive, when compared to individuals communicating face-to-face. Anonymous instant messaging however created a resistance to persuasion in participants, with males showing a significant decline in persuasive outcomes compared to face to face. The findings reveal new insights particularly regarding the interplay of persuasion on gender and modality, with anonymous instant messaging enhancing resistance to persuasion attempts. This study illuminates how varying SE can support new theoretical and applied understandings of how judgments are formed and modified in response to advocacy.

Keywords: applied cognition, persuasion, social media, virtual reality

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Authors: Atbin Mahabbati, Jason Beringer, Matthias Leopold

Abstract:

To address the global challenges of climate and environmental changes, there is a need for quantifying and reducing uncertainties in environmental data, including observations of carbon, water, and energy. Global eddy covariance flux tower networks (FLUXNET), and their regional counterparts (i.e., OzFlux, AmeriFlux, China Flux, etc.) were established in the late 1990s and early 2000s to address the demand. Despite the capability of eddy covariance in validating process modelling analyses, field surveys and remote sensing assessments, there are some serious concerns regarding the challenges associated with the technique, e.g. data gaps and uncertainties. To address these concerns, this research has developed an ensemble model to fill the data gaps of CO₂ flux to avoid the limitations of using a single algorithm, and therefore, provide less error and decline the uncertainties associated with the gap-filling process. In this study, the data of five towers in the OzFlux Network (Alice Springs Mulga, Calperum, Gingin, Howard Springs and Tumbarumba) during 2013 were used to develop an ensemble machine learning model, using five feedforward neural networks (FFNN) with different structures combined with an eXtreme Gradient Boosting (XGB) algorithm. The former methods, FFNN, provided the primary estimations in the first layer, while the later, XGB, used the outputs of the first layer as its input to provide the final estimations of CO₂ flux. The introduced model showed slight superiority over each single FFNN and the XGB, while each of these two methods was used individually, overall RMSE: 2.64, 2.91, and 3.54 g C m⁻² yr⁻¹ respectively (3.54 provided by the best FFNN). The most significant improvement happened to the estimation of the extreme diurnal values (during midday and sunrise), as well as nocturnal estimations, which is generally considered as one of the most challenging parts of CO₂ flux gap-filling. The towers, as well as seasonality, showed different levels of sensitivity to improvements provided by the ensemble model. For instance, Tumbarumba showed more sensitivity compared to Calperum, where the differences between the Ensemble model on the one hand and the FFNNs and XGB, on the other hand, were the least of all 5 sites. Besides, the performance difference between the ensemble model and its components individually were more significant during the warm season (Jan, Feb, Mar, Oct, Nov, and Dec) compared to the cold season (Apr, May, Jun, Jul, Aug, and Sep) due to the higher amount of photosynthesis of plants, which led to a larger range of CO₂ exchange. In conclusion, the introduced ensemble model slightly improved the accuracy of CO₂ flux gap-filling and robustness of the model. Therefore, using ensemble machine learning models is potentially capable of improving data estimation and regression outcome when it seems to be no more room for improvement while using a single algorithm.

Keywords: carbon flux, Eddy covariance, extreme gradient boosting, gap-filling comparison, hybrid model, OzFlux network

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