Search results for: efficiency classification

Authors: Omonov Jahongir Mahmatkulovic

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Purpose: To study the significance of D-dimer (DD), prothrombin time (PT), activated partial thromboplastin time (APTT), thrombin time (TT), and fibrinogen coagulation parameters (Fg) in predicting the course, severity and prognosis of COVID-19. Source and method of research: From September 15, 2021, to November 5, 2021, 93 patients aged 25 to 60 with suspected COVID-19, who are under inpatient treatment at the multidisciplinary clinic of the Tashkent Medical Academy, were retrospectively examined. DD, PT, APTT, and Fg were studied in dynamics and studied changes. Results: Coagulation disorders occurred in the early stages of COVID-19 infection with an increase in DD in 54 (58%) patients and an increase in Fg in 93 (100%) patients. DD and Fg levels are associated with the clinical classification. Of the 33 patients who died, 21 had an increase in DD in the first laboratory study, 27 had an increase in DD in the second and third laboratory studies, and 15 had an increase in PT in the third test. The results of the ROC analysis of mortality showed that the AUC DD was three times 0.721, 0.801, and 0.844, respectively; PT was 0.703, 0.845, and 0.972. (P<0:01). Conclusion”: Coagulation dysfunction is more common in patients with severe and critical conditions. DD and PT can be used as important predictors of mortality from COVID-19.

Keywords: Covid19, DD, PT, Coagulogram analysis, APTT

Procedia PDF Downloads 107

Authors: Almaz R. Gazizov, Sergey S. Kharintsev, Myakzyum Kh. Salakhov

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This work deals with background signal suppression in tip-enhanced near-field optical microscopy (TENOM). The background appears because an optical signal is detected not only from the subwavelength area beneath the tip but also from a wider diffraction-limited area of laser’s waist that might contain another substance. The background can be reduced by using a taper probe with a grating on its lateral surface where an external illumination causes surface plasmon excitation. It requires the grating with parameters perfectly matched with a given incident light for effective light coupling. This work is devoted to an analysis of the light-grating coupling and a quest of grating parameters to enhance a near-field light beneath the tip apex. The aim of this work is to find the figure of merit of plasmon excitation depending on grating period and location of grating in respect to the apex. In our consideration the metallic grating on the lateral surface of the tapered plasmonic probe is illuminated by a plane wave, the electric field is perpendicular to the sample surface. Theoretical model of efficiency of plasmon excitation and propagation toward the apex is tested by fdtd-based numerical simulation. An electric field of the incident light is enhanced on the grating by every single slit due to lightning rod effect. Hence, grating causes amplitude and phase modulation of the incident field in various ways depending on geometry and material of grating. The phase-modulating grating on the probe is a sort of metasurface that provides manipulation by spatial frequencies of the incident field. The spatial frequency-dependent electric field is found from the angular spectrum decomposition. If one of the components satisfies the phase-matching condition then one can readily calculate the figure of merit of plasmon excitation, defined as a ratio of the intensities of the surface mode and the incident light. During propagation towards the apex, surface wave undergoes losses in probe material, radiation losses, and mode compression. There is an optimal location of the grating in respect to the apex. One finds the value by matching quadratic law of mode compression and the exponential law of light extinction. Finally, performed theoretical analysis and numerical simulations of plasmon excitation demonstrate that various surface waves can be effectively excited by using the overtones of a period of the grating or by phase modulation of the incident field. The gratings with such periods are easy to fabricate. Tapered probe with the grating effectively enhances and localizes the incident field at the sample.

Keywords: angular spectrum decomposition, efficiency, grating, surface plasmon, taper nanoantenna

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Authors: Dina Magdy Abdo, Ayat N. El-Shazly, Hamdy Maamoun Abdel-Ghafar, E. A. Abdel-Aal

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Forward osmosis (FO) is one of the important processes during the wastewater treatment system for environmental remediation and fresh water regeneration. Both Egypt and China are troubled by over millions of tons of wastewater every year, including domestic and industrial wastewater. However, traditional FO process in wastewater treatment usually suffers low efficiency and high energy consumption because of the continuously diluted draw solution. An additional concentration process is necessary to keep running of FO separation, causing energy waste. Based on the previous study on photothermal membrane, a sun-driven evaporation process is integrated into the draw solution side of FO system. During the sun-driven evaporation, not only the draw solution can be concentrated to maintain a stable and sustainable FO system, but fresh water can be directly separated for regeneration. Solar energy is the ultimate energy source of everything we have on Earth and is, without any doubt, the most renewable and sustainable energy source available to us. Additionally, the FO membrane process is rationally designed to limit the concentration polarization and fouling. The FO membrane’s structure and surface property will be further optimized by the adjustment of the doping ratio of controllable nano-materials, membrane formation conditions, and selection of functional groups. A novel kind of nano-composite functional separation membrane with bi-interception layers and high hydrophilicity will be developed for the application in wastewater treatment. So, herein we aim to design a new wastewater treatment system include forward osmosis with high-efficiency energy recovery via the integration of photothermal membrane.

Keywords: forword, membrane, solar, water treatment

Procedia PDF Downloads 81

Authors: Mehmet Akif Aslan, Mehmet Simsek, Eyup Sensoy

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Big Data stands for today’s cutting-edge technology. As the technology becomes widespread, so does Data. Utilizing massive data sets enable companies to get competitive advantages over their adversaries. Out of many area of Big Data usage, logistics has significance role in both commercial sector and military. This paper lays out what big data is and how it is used in both military and commercial logistics.

Keywords: big data, logistics, operational efficiency, risk management

Procedia PDF Downloads 641

Authors: Kamel Laidi, Khelifa Benmansour, Ouahid Bouchhida

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Neural networks-based approach for 2-cells serial converter has been developed and implemented. The approach is based on a behavioural description of the different operating modes of the converter. Each operating mode represents a well-defined configuration, and for which is matched an operating zone satisfying given invariance conditions, depending on the capacitors' voltages and the load current of the converter. For each mode, a control vector whose components are the control signals to be applied to the converter switches has been associated. Therefore, the problem is reduced to a classification task of the different operating modes of the converter. The artificial neural nets-based approach, which constitutes a powerful tool for this kind of task, has been adopted and implemented. The application to a 2-cells chopper has allowed ensuring efficient and robust control of the load current and a high capacitors voltages balancing.

Keywords: neural nets, control, multicellular converters, 2-cells chopper

Procedia PDF Downloads 834

Authors: Supakij Saisopon

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The process of economic development incurs spatial transformation. This spatial alternation also causes environmental impacts, leading to higher pollution. In the case of Thailand, there is still a lack of price-endogenous quantitative analysis incorporating relationships among economic growth, land-use change, and environmental impact. Therefore, this paper aimed at developing the Computable General Equilibrium (CGE) model with the capability of stimulating such mutual effects. The developed CGE model has also incorporated the nested constant elasticity of transformation (CET) structure that describes the spatial redistribution mechanism between agricultural land and urban area. The simulation results showed that the 1% decrease in the availability of agricultural land lowers the value-added of agricultural by 0.036%. Similarly, the 1% reduction of availability of urban areas can decrease the value-added of manufacturing and service sectors by 0.05% and 0.047%, respectively. Moreover, the outcomes indicate that the increasing farming and urban areas induce higher volumes of solid waste, wastewater, and air pollution. Specifically, the 1% increase in the urban area can increase pollution as follows: (1) the solid waste increase by 0.049%, (2) water pollution ̶ indicated by biochemical oxygen demand (BOD) value ̶ increase by 0.051% and (3) air pollution ̶ indicated by the volumes of CO₂, N₂O, NOₓ, CH₄, and SO₂ ̶ increase within the range of 0.045%–0.051%. With the simulation for exploring the sustainable development path, a 1% increase in agricultural land use efficiency leads to the shrinking demand for agricultural land. But this is not happening in urban, a 1% scale increase in urban utilization results in still increasing demand for land. Therefore, advanced clean production technology is necessary to align the increasing land-use efficiency with the lowered pollution density.

Keywords: CGE model, CET structure, environmental impact, land use

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Authors: Emad A. Mohammed

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Utilizing the capabilities of Data Mining and Artificial Intelligence in the prediction of the minimum miscibility pressure (MMP) required for multi-contact miscible (MCM) displacement of reservoir petroleum by hydrocarbon gas flooding using Fuzzy Logic models and Artificial Neural Network models will help a lot in giving accurate results. The factors affecting the (MMP) as it is proved from the literature and from the dataset are as follows: XC2-6: Intermediate composition in the oil-containing C2-6, CO2 and H2S, in mole %, XC1: Amount of methane in the oil (%),T: Temperature (°C), MwC7+: Molecular weight of C7+ (g/mol), YC2+: Mole percent of C2+ composition in injected gas (%), MwC2+: Molecular weight of C2+ in injected gas. Fuzzy Logic and Neural Networks have been used widely in prediction and classification, with relatively high accuracy, in different fields of study. It is well known that the Fuzzy Inference system can handle uncertainty within the inputs such as in our case. The results of this work showed that our proposed models perform better with higher performance indices than other emprical correlations.

Keywords: MMP, gas flooding, artificial intelligence, correlation

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Authors: Evgen Prokhorov, Gabriel Luna-Barcenas

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The polysaccharide chitosan (CS) is an attractive biopolymer for the stabilization of several nanoparticles in acidic aqueous media. This is due in part to the presence of abundant primary NH2 and OH groups which may lead to steric or chemical stabilization. Applications of most CS nanocomposites are based upon the interaction of high surface area nanoparticles (NPs) with different substance. Therefore, agglomeration of NPs leads to decreasing effective surface area such that it may decrease the efficiency of nanocomposites. The aim of this work is to measure nanocomposite’s electrical conductivity phenomena that will allow one to formulate optimal concentrations of conductivity NPs in CS-based nanocomposites. Additionally, by comparing the efficiency of such nanocomposites, one can guide applications in the biomedical (antibacterial properties and tissue regeneration) and sensor fields (detection of copper and nitrate ions in aqueous solutions). It was shown that the best antibacterial (CS-AgNPs, CS-AgNPs-carbon nanotubes) and would healing properties (CS-AuNPs) are observed in nanocomposites with concentrations of NPs near the percolation threshold. In this regard, the best detection limit in potentiometric and impedimetric sensors for detection of copper ions (using CS-AuNPs membrane) and nitrate ions (using CS-clay membrane) in aqueous solutions have been observed for membranes with concentrations of NPs near percolation threshold. It is well known that at the percolation concentration of NPs an abrupt increasing of conductivity is observed due to the presence of physical contacts between NPs; above this concentration, agglomeration of NPs takes place such that a decrease in the effective surface and performance of nanocomposite appear. The obtained relationship between electrical percolation threshold and performance of polymer nanocomposites with conductivity NPs is important for the design and optimization of polymer-based nanocomposites for different applications.

Keywords: chitosan, conductivity nanoparticles, percolation threshold, polymer nanocomposites

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Authors: Alvaro Ramírez, Martín Muñoz-Morales, Ester López- Fernández, Javier Llanos, C. Ania

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Carbon blacks are value-added materials typically produced through the incomplete combustion or thermal decomposition of hydrocarbons. Traditionally, they have been used as catalysts in many different applications, but in the last decade, their potential in green chemistry has gained significant attention. Among them, the electrochemical production of H₂O₂ has attracted interest because of their properties as high oxidant capacity or their industrial interest as a bleaching agent. Carbon blacks are commonly used in this application in a catalytic ink that is drop-casted on supporting electrodes and acts as catalysts for the electrochemical production of H₂O₂ through oxygen reduction reaction (ORR). However, the different structural and electrochemical behaviors of each type of carbon black influence their applications. In this line, the term ‘carbon black’, has to be considered as a generic name that does not guarantee any physicochemical properties if any further description is mentioned. In fact, different specific surface area (SSA), surface functional groups, porous structure, and electro catalysts effect seem very important for electrochemical applications, and considerable differences were found during the analysis of four types of carbon blacks. Thus, the aim of this work is to evaluate the influence of SSA, porous structure, oxygen functional groups, and structural defects to differentiate among these carbon blacks (e.g. Vulcan XC72, Superior Graphite Co, Printex XE2, and Prolabo) for H₂O₂ production via ORR, using carbon paper as electrode support with improved selectivity and efficiency. Results indicate that the number and size of pores, along with surface functional groups, are key parameters that significantly affect the overall process efficiency.

Keywords: carbon blacks, oxygen reduction reaction, hydrogen peroxide, porosity, surface functional groups

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Authors: Mesfin Angaw Tesfay

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L-Xylulose is a potentially valuable rare sugar used as starting material for antiviral and anticancer drug development in pharmaceutical industries. L-Xylulose exist in a very low concentration in nature and have to be synthesized from cheap starting materials such as xylitol through biotechnological approaches. In this study, cofactor engineering and deep eutectic solvent were applied to improve the efficiency of L-xylulose production from xylitol. A water-forming NAD+ regeneration enzyme (NADH oxidase) from Streptococcus mutans ATCC 25175 was introduced into E. coli with xylitol-4-dehydrogenase (XDH) of Pantoea ananatis resulting in recombinant cells harboring the vector pETDuet-xdh-SmNox. Further, three deep eutectic solvents (DES) including, Choline chloride/glycerol (ChCl/G), Choline chloride/urea (ChCl/U), and Choline chloride/ethylene glycol (ChCl/EG) have been employed to facilitate the conversion efficiency of L-xylulose from xylitol. The co-expression system exhibited optimal activity at a temperature of 37 ℃ and pH 8.5, and the addition of Mg2+ enhanced the catalytic activity by 1.19-fold. Co-expression of NADH oxidase with XDH enzyme resulted in increased L-xylulose concentration and productivity from xylitol as well as the intracellular NAD+ concentration. Two of the DES used (ChCl/U and ChCl/EG) show positive effects on product yield and the ChCl/G has inhibiting effects. The optimum concentration of ChCl/U was 2.5%, which increased the L-xylulose yields compared to the control without DES. In a 1 L fermenter the final concentration and productivity of L-xylulose from 50 g/L of xylitol reached 48.45 g/L, and 2.42 g/L.h respectively, which was the highest report. Overall, this study is a suitable approach for large-scale production of L-xylulose from xylitol using the engineered E. coli cell.

Keywords: Xylitol-4-dehydrogenase, NADH oxidase, L-xylulose, Xylitol, Coexpression, DESs

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Authors: T. Punjansing, M. Nakkuntod, S. Homchan, P. Inthima, A. Kongbangkerd

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The influence of organic supplements on growth and multiplication efficiency of Phaius tankervilleae var. alba seedlings was investigated. 12 week-old seedlings were cultured on half-strength semi-solid Murashige and Skoog (MS) medium supplemented with 30 g/L sucrose, 8 g/L agar and various concentrations of coconut water (0, 50, 100, 150 and 200 mL/L) combined with potato extract (0, 25 and 50 g/L) and the pH was adjusted to 5.8 prior to autoclaving. The cultures were then kept under constant photoperiod (16 h light: 8 h dark) at 25 ± 2 °C for 12 weeks. The highest number of shoots (3.0 shoots/explant) was obtained when cultured on the medium added with 50 ml/L coconut water and 50 g/L potato extract whereas the highest number of leaves (5.9 leaves/explant) and roots (6.1 roots/explant) could receive on the medium supplemented with 150 ml/L coconut water and 50 g/L potato extract. with 150 ml/L coconut water and 50 g/L potato extract. Additionally, plantlets of P. tankervilleae var. alba were transferred to grow into seven different substrates i.e. soil, sand, coconut husk chip, soil-sand mix (1: 1), soil-coconut husk chip mix (1: 1), sand-coconut husk chip mix (1: 1) and soil-sand-coconut husk chip mix (1: 1: 1) for four weeks. The results found that acclimatized plants showed 100% of survivals when sand, coconut husk chip and sand-coconut husk chip mix are used as substrates. The number of leaves induced by sand-coconut husk chip mix was significantly higher than that planted in other substrates (P > 0.05). Meanwhile, no significant difference in new shoot formation among these substrates was observed (P < 0.05). This precursory developing protocol was likely to be applied for more large scale of plant production as well as conservation of germplasm of this orchid species.

Keywords: organic supplements, acclimatization, Phaius tankervilleae var. alba, orchid

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Authors: Surah Aldakhl, Dafer Alali, Mohamed Zohdy

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The next generation of electricity grid infrastructure, known as the "smart grid," integrates smart ICT (information and communication technology) into existing grids in order to alleviate the drawbacks of existing one-way grid systems. Future power systems' efficiency and dependability are anticipated to significantly increase thanks to the Smart Grid, especially given the desire for renewable energy sources. The security of the Smart Grid's cyber infrastructure is a growing concern, though, as a result of the interconnection of significant power plants through communication networks. Since cyber-attacks can destroy energy data, beginning with personal information leaking from grid members, they can result in serious incidents like huge outages and the destruction of power network infrastructure. We shall thus propose a secure smart energy management system based on the Blockchain as a remedy for this problem. The power transmission and distribution system may undergo a transformation as a result of the inclusion of optical fiber sensors and blockchain technology in smart grids. While optical fiber sensors allow real-time monitoring and management of electrical energy flow, Blockchain offers a secure platform to safeguard the smart grid against cyberattacks and unauthorized access. Additionally, this integration makes it possible to see how energy is produced, distributed, and used in real time, increasing transparency. This strategy has advantages in terms of improved security, efficiency, dependability, and flexibility in energy management. An in-depth analysis of the advantages and drawbacks of combining blockchain technology with optical fiber is provided in this paper.

Keywords: smart grids, blockchain, fiber optic sensor, security

Procedia PDF Downloads 120

Authors: Anna Carolina De Paula Sermarini, Rodrigo Flora Calili

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Energy Efficiency (EE) needs investments, new technologies, greater awareness and management on the side of citizens and organizations, and more planning. However, this issue is usually remembered and discussed only in moments of energy crises, and opportunities are missed to take better advantage of the potential of EE in the various sectors of the economy. In addition, there is little concern about the subject among the less favored classes, especially in low-income communities. Accordingly, this article presents suggestions for public policies that aim to increase EE for low-income housing and communities based on international and national experiences. After reviewing the literature, eight policies were listed, and to evaluate them; a multicriteria decision model was developed using the AHP (Analytical Hierarchy Process) and TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) methods, combined with fuzzy logic. Nine experts analyzed the policies according to 9 criteria: economic impact, social impact, environmental impact, previous experience, the difficulty of implementation, possibility/ease of monitoring and evaluating the policies, expected impact, political risks, and public governance and sustainability of the sector. The results found in order of preference are (i) Incentive program for equipment replacement; (ii) Community awareness program; (iii) EE Program with a greater focus on low income; (iv) Staggered and compulsory certification of social interest buildings; (v) Programs for the expansion of smart metering, energy monitoring and digitalization; (vi) Financing program for construction and retrofitting of houses with the emphasis on EE; (vii) Income tax deduction for investment in EE projects in low-income households made by companies; (viii) White certificates of energy for low-income. First, the policy of equipment substitution has been employed in Brazil and the world and has proven effective in promoting EE. For implementation, efforts are needed from the federal and state governments, which can encourage companies to reduce prices, and provide some type of aid for the purchase of such equipment. In second place is the community awareness program, promoting socio-educational actions on EE concepts and with energy conservation tips. This policy is simple to implement and has already been used by many distribution utilities in Brazil. It can be carried out through bids defined by the government in specific areas, being executed by third sector companies with public and private resources. Third on the list is the proposal to continue the Energy Efficiency Program (which obliges electric energy companies to allocate resources for research in the area) by suggesting the return of the mandatory investment of 60% of the resources in projects for low income. It is also relatively simple to implement, requiring efforts by the federal government to make it mandatory, and on the part of the distributors, compliance is needed. The success of the suggestions depends on changes in the established rules and efforts from the interested parties. For future work, we suggest the development of pilot projects in low-income communities in Brazil and the application of other multicriteria decision support methods to compare the results obtained in this study.

Keywords: energy efficiency, low-income community, public policy, multicriteria decision making

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Authors: Pramod Jagtap, Kisan Jadhav, Neha Dand

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Risperidone (RISP) is an antipsychotic agent and has low water solubility and nontargeted delivery results in numerous side effects. Hence, an attempt was made to develop SLNs hydrogel for intranasal delivery of RISP to achieve maximum bioavailability and reduction of side effects. RISP loaded SLNs composed of 1.65% (w/v) lipid mass were produced by high shear homogenization (HSH) coupled ultrasound (US) method using glyceryl monostearate (GMS) or Imwitor 900K (solid lipid). The particles were loaded with 0.2% (w/v) of the RISP & surface-tailored with a 2.02% (w/v) non-ionic surfactant Tween® 80. Optimization was done using 32 factorial design using Design Expert® software. The prepared SLNs dispersion incorporated into Polycarbophil AA1 hydrogel (0.5% w/v). The final gel formulation was evaluated for entrapment efficiency, particle size, rheological properties, X ray diffraction, in vitro diffusion, ex vivo permeation using sheep nasal mucosa and histopathological studies for nasocilliary toxicity. The entrapment efficiency of optimized SLNs was found to be 76 ± 2 %, polydispersity index <0.3., particle size 278 ± 5 nm. This optimized batch was incorporated into hydrogel. The pH was found to be 6.4 ± 0.14. The rheological behaviour of hydrogel formulation revealed no thixotropic behaviour. In histopathology study, there was no nasocilliary toxicity observed in nasal mucosa after ex vivo permeation. X-ray diffraction data shows drug was in amorphous form. Ex vivo permeation study shows controlled release profile of drug.

Keywords: ex vivo, particle size, risperidone, solid lipid nanoparticles

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Authors: Mercedes Bertotto, Marcelo Bello, Hector Goicoechea, Veronica Fusca

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The National Service of Agri-Food Health and Quality (SENASA), controls honey to detect contamination by synthetic or natural chemical substances and establishes and controls the traceability of the product. The utility of near-infrared spectroscopy for the detection of adulteration of honey with high fructose corn syrup (HFCS) was investigated. First of all, a mixture of different authentic artisanal Argentinian honey was prepared to cover as much heterogeneity as possible. Then, mixtures were prepared by adding different concentrations of high fructose corn syrup (HFCS) to samples of the honey pool. 237 samples were used, 108 of them were authentic honey and 129 samples corresponded to honey adulterated with HFCS between 1 and 10%. They were stored unrefrigerated from time of production until scanning and were not filtered after receipt in the laboratory. Immediately prior to spectral collection, honey was incubated at 40°C overnight to dissolve any crystalline material, manually stirred to achieve homogeneity and adjusted to a standard solids content (70° Brix) with distilled water. Adulterant solutions were also adjusted to 70° Brix. Samples were measured by NIR spectroscopy in the range of 650 to 7000 cm⁻¹. The technique of specular reflectance was used, with a lens aperture range of 150 mm. Pretreatment of the spectra was performed by Standard Normal Variate (SNV). The ant colony optimization genetic algorithm sample selection (ACOGASS) graphical interface was used, using MATLAB version 5.3, to select the variables with the greatest discriminating power. The data set was divided into a validation set and a calibration set, using the Kennard-Stone (KS) algorithm. A combined method of Potential Functions (PF) was chosen together with Partial Least Square Linear Discriminant Analysis (PLS-DA). Different estimators of the predictive capacity of the model were compared, which were obtained using a decreasing number of groups, which implies more demanding validation conditions. The optimal number of latent variables was selected as the number associated with the minimum error and the smallest number of unassigned samples. Once the optimal number of latent variables was defined, we proceeded to apply the model to the training samples. With the calibrated model for the training samples, we proceeded to study the validation samples. The calibrated model that combines the potential function methods and PLSDA can be considered reliable and stable since its performance in future samples is expected to be comparable to that achieved for the training samples. By use of Potential Functions (PF) and Partial Least Square Linear Discriminant Analysis (PLS-DA) classification, authentic honey and honey adulterated with HFCS could be identified with a correct classification rate of 97.9%. The results showed that NIR in combination with the PT and PLS-DS methods can be a simple, fast and low-cost technique for the detection of HFCS in honey with high sensitivity and power of discrimination.

Keywords: adulteration, multivariate analysis, potential functions, regression

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Authors: Amiza Surmi, Azmi Shariff, Sow Mun Serene Lock

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In recent decades, there has been a significant emphasis on the pivotal role of Rotating Packed Beds (RPBs) in absorption processes, encompassing the removal of Volatile Organic Compounds (VOCs) from groundwater, deaeration, CO2 absorption, desulfurization, and similar critical applications. The primary focus is elevating mass transfer rates, enhancing separation efficiency, curbing power consumption, and mitigating pressure drops. Additionally, substantial efforts have been invested in exploring the adaptation of RPB technology for offshore deployment. This comprehensive study delves into the intricacies of nitrogen removal under low temperature and high-pressure conditions, employing the high gravity principle via innovative RPB distillation concept with a specific emphasis on optimizing mass transfer. Based on the author's knowledge and comprehensive research, no cryogenic experimental testing was conducted to remove nitrogen via RPB. The research identifies pivotal process control factors through meticulous experimental testing, with pressure, reflux ratio, and reboil ratio emerging as critical determinants in achieving the desired separation performance. The results are remarkable, with nitrogen removal reaching less than one mole% in the Liquefied Natural Gas (LNG) product and less than three moles% methane in the nitrogen-rich gas stream. The study further unveils the mass transfer coefficient, revealing a noteworthy trend of decreasing Number of Transfer Units (NTU) and Area of Transfer Units (ATU) as the rotational speed escalates. Notably, the condenser and reboiler impose varying demands based on the operating pressure, with lower pressures at 12 bar requiring a more substantial duty than the 15-bar operation of the RPB. In pursuit of optimal energy efficiency, a meticulous sensitivity analysis is conducted, pinpointing the ideal combination of pressure and rotating speed that minimizes overall energy consumption. These findings underscore the efficiency of the RPB distillation approach in effecting efficient separation, even when operating under the challenging conditions of low temperature and high pressure. This achievement is attributed to a rigorous process control framework that diligently manages the operational pressure and temperature profile of the RPB. Nonetheless, the study's conclusions point towards the need for further research to address potential scaling challenges and associated risks, paving the way for the industrial implementation of this transformative technology.

Keywords: mass transfer coefficient, nitrogen removal, liquefaction, rotating packed bed

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Authors: Guneet Saini, Shahrukh, Sunil Sharma

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Traffic congestion is the most critical issue faced by those in the transportation profession today. Over the past few years, roundabouts have been recognized as a measure to promote efficiency at intersections globally. In developing countries like India, this type of intersection still faces a lot of issues, such as bottleneck situations, long queues and increased waiting times, due to increasing traffic which in turn affect the performance of the entire urban network. This research is a case study of a non-conventional roundabout, in terms of geometric design, in a small town in India. These types of roundabouts should be analyzed for their functionality in mixed traffic conditions, prevalent in many developing countries. Microscopic traffic simulation is an effective tool to analyze traffic conditions and estimate various measures of operational performance of intersections such as capacity, vehicle delay, queue length and Level of Service (LOS) of urban roadway network. This study involves analyzation of an unsymmetrical non-circular 6-legged roundabout known as “Kala Aam Chauraha” in a small town Bulandshahr in Uttar Pradesh, India using VISSIM simulation package which is the most widely used software for microscopic traffic simulation. For coding in VISSIM, data are collected from the site during morning and evening peak hours of a weekday and then analyzed for base model building. The model is calibrated on driving behavior and vehicle parameters and an optimal set of calibrated parameters is obtained followed by validation of the model to obtain the base model which can replicate the real field conditions. This calibrated and validated model is then used to analyze the prevailing operational traffic performance of the roundabout which is then compared with a proposed alternative to improve efficiency of roundabout network and to accommodate pedestrians in the geometry. The study results show that the alternative proposed is an advantage over the present roundabout as it considerably reduces congestion, vehicle delay and queue length and hence, successfully improves roundabout performance without compromising on pedestrian safety. The study proposes similar designs for modification of existing non-conventional roundabouts experiencing excessive delays and queues in order to improve their efficiency especially in the case of developing countries. From this study, it can be concluded that there is a need to improve the current geometry of such roundabouts to ensure better traffic performance and safety of drivers and pedestrians negotiating the intersection and hence this proposal may be considered as a best fit.

Keywords: operational performance, roundabout, simulation, VISSIM

Procedia PDF Downloads 139

Authors: Lu Song Ma Li Zhi

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With the rapid advancement of artificial intelligence technology, large language models (LLMs) have become a milestone in the field of natural language processing, demonstrating remarkable capabilities in semantic understanding, intelligent question answering, and text generation. These models are gradually penetrating various industries, particularly showcasing significant application potential in the data analysis domain. However, retraining or fine-tuning these models requires substantial computational resources and ample downstream task datasets, which poses a significant challenge for many enterprises and research institutions. Without modifying the internal parameters of the large models, prompt engineering techniques can rapidly adapt these models to new domains. This paper proposes a prompt design strategy aimed at leveraging the capabilities of large language models to automate the generation of data analysis code. By carefully designing prompts, data analysis requirements can be described in natural language, which the large language model can then understand and convert into executable data analysis code, thereby greatly enhancing the efficiency and convenience of data analysis. This strategy not only lowers the threshold for using large models but also significantly improves the accuracy and efficiency of data analysis. Our approach includes requirements for the precision of natural language descriptions, coverage of diverse data analysis needs, and mechanisms for immediate feedback and adjustment. Experimental results show that with this prompt design strategy, large language models perform exceptionally well in multiple data analysis tasks, generating high-quality code and significantly shortening the data analysis cycle. This method provides an efficient and convenient tool for the data analysis field and demonstrates the enormous potential of large language models in practical applications.

Keywords: large language models, prompt design, data analysis, code generation

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Authors: Haojie Wang, Xun Li, Rui Yin

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In order to solve the memorization overfitting in the meta-learning MAML algorithm, a method of generating mutually exclusive tasks based on data augmentation is proposed. This method generates a mutex task by corresponding one feature of the data to multiple labels, so that the generated mutex task is inconsistent with the data distribution in the initial dataset. Because generating mutex tasks for all data will produce a large number of invalid data and, in the worst case, lead to exponential growth of computation, this paper also proposes a key data extraction method, that only extracts part of the data to generate the mutex task. The experiments show that the method of generating mutually exclusive tasks can effectively solve the memorization overfitting in the meta-learning MAML algorithm.

Keywords: data augmentation, mutex task generation, meta-learning, text classification.

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Authors: Nabil Al-Zaqri

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Semiconductor photocatalysts with surface defects display incredible light absorption bandwidth, and these defects function as highly active sites for oxidation processes by interacting with the surface band structure. Accordingly, engineering the photocatalyst with surface oxygen vacancies will enhance the semiconductor nanostructure's photocatalytic efficiency. Herein, a CeO2₋ₓ nanostructure is designed under the influence of low-frequency ultrasonic waves to create surface oxygen vacancies. This approach enhances the photocatalytic efficiency compared to many heterostructures while keeping the intrinsiccrystal structure intact. Ultrasonic waves induce the acoustic cavitation effect leading to the dissemination of active elements on the surface, which results in vacancy formation in conjunction with larger surface area and smaller particle size. The structural analysis of CeO₂₋ₓ revealed higher crystallinity, as well as morphological optimization, and the presence of oxygen vacancies is verified through Raman, X-rayphotoelectron spectroscopy, temperature-programmed reduction, photoluminescence, and electron spinresonance analyses. Oxygen vacancies accelerate the redox cycle between Ce₄+ and Ce₃+ by prolongingphotogenerated charge recombination. The ultrasound-treated pristine CeO₂ sample achieved excellenthydrogen production showing a quantum efficiency of 1.125% and efficient organic degradation. Ourpromising findings demonstrated that ultrasonic treatment causes the formation of surface oxygenvacancies and improves photocatalytic hydrogen evolution and pollution degradation. Conclusion: Defect engineering of the ceria nanoparticles with oxygen vacancies was achieved for the first time using low-frequency ultrasound treatment. The U-CeO₂₋ₓsample showed high crystallinity, and morphological changes were observed. Due to the acoustic cavitation effect, a larger surface area and small particle size were observed. The ultrasound treatment causes particle aggregation and surface defects leading to oxygen vacancy formation. The XPS, Raman spectroscopy, PL spectroscopy, and ESR results confirm the presence of oxygen vacancies. The ultrasound-treated sample was also examined for pollutant degradation, where 1O₂was found to be the major active species. Hence, the ultrasound treatment influences efficient photocatalysts for superior hydrogen evolution and an excellent photocatalytic degradation of contaminants. The prepared nanostructure showed excellent stability and recyclability. This work could pave the way for a unique post-synthesis strategy intended for efficient photocatalytic nanostructures.

Keywords: surface defect, CeO₂₋ₓ, photocatalytic, water treatment, H₂ production

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Authors: Marcello De Falco, Gianluca Natrella, Mauro Capocelli

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CO₂ capture and utilization (CCU) is a promising approach to reduce GHG(greenhouse gas) emissions. Many technologies in this field are recently attracting attention. However, since CO₂ is a very stable compound, its utilization as a reagent is energetic intensive. As a consequence, it is unclear whether CCU processes allow for a net reduction of environmental impacts from a life cycle perspective and whether these solutions are sustainable. Among the tools to apply for the quantification of the real environmental benefits of CCU technologies, exergy analysis is the most rigorous from a scientific point of view. The exergy of a system is the maximum obtainable work during a process that brings the system into equilibrium with its reference environment through a series of reversible processes in which the system can only interact with such an environment. In other words, exergy is an “opportunity for doing work” and, in real processes, it is destroyed by entropy generation. The exergy-based analysis is useful to evaluate the thermodynamic inefficiencies of processes, to understand and locate the main consumption of fuels or primary energy, to provide an instrument for comparison among different process configurations and to detect solutions to reduce the energy penalties of a process. In this work, the exergy analysis of a process for the production of Dimethyl Ether (DME) from green hydrogen generated through an electrolysis unit and pure CO₂ captured from flue gas is performed. The model simulates the behavior of all units composing the plant (electrolyzer, carbon capture section, DME synthesis reactor, purification step), with the scope to quantify the performance indices based on the II Law of Thermodynamics and to identify the entropy generation points. Then, a plant optimization strategy is proposed to maximize the exergy efficiency.

Keywords: green DME production, exergy analysis, energy penalties, exergy efficiency

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Authors: Dalwinder Singh, Amandeep Verma, Manpreet Kaur, Birmohan Singh

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The detection of pre-cancerous changes using a Pap smear test of cervical cell is the important step for the early diagnosis of cervical cancer. The Pap smear test consists of a sample of human cells taken from the cervix which are analysed to detect cancerous and pre-cancerous stage of the given subject. The manual analysis of these cells is labor intensive and time consuming process which relies on expert cytotechnologist. In this paper, a computer assisted system for the automated analysis of the cervical cells has been proposed. We propose a morphology based approach to the nucleus detection and segmentation of the cytoplasmic region of the given single or multiple overlapped cell. Further, various texture and region based features are calculated from these cells to classify these into normal and abnormal cell. Experimental results on public available dataset show that our system has achieved satisfactory success rate.

Keywords: cervical cancer, cervical tissue, mathematical morphology, texture features

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Authors: Levent Basayigit, Mert Dedeoglu, Fadime Ozogul

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In this study, an application was carried out to determine the Volcanic Soils by using remote sensing.  The study area was located on the Golcuk formation in Isparta-Turkey. The thermal bands of Landsat 7 image were used for processing. The implementation of the climate model that was based on the water index was used in ERDAS Imagine software together with pixel based image classification. Soil Moisture Index (SMI) was modeled by using the surface temperature (Ts) which was obtained from thermal bands and vegetation index (NDVI) derived from Landsat 7. Surface moisture values were grouped and classified by using scoring system. Thematic layers were compared together with the field studies. Consequently, different moisture levels for volcanic soils were indicator for determination and separation. Those thermal wavelengths are preferable bands for separation of volcanic soils using moisture and temperature models.

Keywords: Landsat 7, soil moisture index, temperature models, volcanic soils

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Authors: Wahab Ali, Oyebade K. Oyedotun, Adnan Khashman

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Breast cancer remains a threat to the woman’s world in view of survival rates, it early diagnosis and mortality statistics. So far, research has shown that many survivors of breast cancer cases are in the ones with early diagnosis. Breast cancer is usually categorized into stages which indicates its severity and corresponding survival rates for patients. Investigations show that the farther into the stages before diagnosis the lesser the chance of survival; hence the early diagnosis of breast cancer becomes imperative, and consequently the application of novel technologies to achieving this. Over the year, mammograms have used in the diagnosis of breast cancer, but the inconclusive deductions made from such scans lead to either false negative cases where cancer patients may be left untreated or false positive where unnecessary biopsies are carried out. This paper presents the application of artificial neural networks in the prediction of severity of breast tumour (whether benign or malignant) using mammography reports and other factors that are related to breast cancer.

Keywords: breast cancer, intelligent classification, neural networks, mammography

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Authors: Marko Batic, Nikola Tomasevic, Sanja Vranes

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One of the strongholds in the ubiquitous efforts related to the energy conservation and energy efficiency improvement is represented by the retrofit of high energy consumers in buildings. In general, HVAC systems represent the highest energy consumers in buildings. However they usually suffer from mal-operation and/or malfunction, causing even higher energy consumption than necessary. Various Fault Detection and Diagnosis (FDD) systems can be successfully employed for this purpose, especially when it comes to the application at a single device/unit level. In the case of more complex systems, where multiple devices are operating in the context of the same building, significant energy efficiency improvements can only be achieved through application of comprehensive FDD systems relying on additional higher level knowledge, such as their geographical location, served area, their intra- and inter- system dependencies etc. This paper presents a comprehensive FDD system that relies on the utilization of common knowledge repository that stores all critical information. The discussed system is deployed as a test-bed platform at the two at Fiumicino and Malpensa airports in Italy. This paper aims at presenting advantages of implementation of the knowledge base through the utilization of ontology and offers improved functionalities of such system through examples of typical queries and reasoning that enable derivation of high level energy conservation measures (ECM). Therefore, key SPARQL queries and SWRL rules, based on the two instantiated airport ontologies, are elaborated. The detection of high level irregularities in the operation of airport heating/cooling plants is discussed and estimation of energy savings is reported.

Keywords: airport ontology, knowledge management, ontology modeling, reasoning

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Authors: Hafiza Mamoona Khalid, Afshan Mujahid, Asif Ali, Asim Laeeq Khan, Mahmood Saleem, Rafael M. Santos

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The ever-escalating CO₂ concentration in the atmosphere calls for accelerated development and deployment of carbon capture processes to reduce emissions. Mixed matrix membranes (MMMs), which are fabricated by incorporating the beneficial properties of highly selective inorganic fillers into a polymer matrix, have exhibited significant progress and the ability to enhance the performance of a membrane for gas separation. In this research, an amine-based ionic liquid (IL) [APTMS][AC] was prepared, which has greater CO₂ affinity and greater solubility due to its amine moiety. The metal–organic framework (MOF) UiO-66 with a multidimensional crystalline structure was used as a filler due to its appropriate porosity and tunable properties, and it was functionalized with NH₂. MOFs were further modified with an IL to prepare UiO-66@IL and UiO-66-NH₂@IL, and MMMs incorporating each MOF were fabricated with the polymer Pebax-1657. All the prepared membranes and MOFs were characterized to predict their separation efficiency. Several characterization techniques, namely, FTIR spectroscopy, XRD, and SEM, were used to successfully synthesize UiO-66@IL and UiO-66-NH₂@IL composites and confirmed proper dispersion and excellent polymer‒ filler compatibility at filler loadings ranging from 0 to 30 wt.%. The separation performances were investigated, and the results showed that the incorporation of RTIL with the highly crystalline structure and large surface area of UiO-66 enhanced the separation efficiency of the membrane. The permeability of CO₂ for all fabricated membranes continuously increased with increasing filler concentration, wherein the permeability was comparatively high for the UiO-66-NH₂ MMMs. The CO₂/CH₄ selectivity improved by 35%, 54%, and 60%, respectively, for UiO-66@IL, UiO-66-NH₂, and UiO-66-NH₂@IL MMMs compared to simple UiO-66 for CO₂/CH₄ and by 28%, 36%, and 63%, respectively, for CO₂/N₂, with an increase in filler loading in the MMMs.

Keywords: gas separation, mixed matrix membranes, CO₂ sequestration, climate change, global warming

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Authors: Taslim Khan, Ray Hua Horng, Rajendra Singh

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This study presents a comprehensive analysis of the design, fabrication, and performance evaluation of a solar-blind Schottky photodetector based on ZnGa₂O₄ grown via MOCVD, utilizing Ni/Au as the Schottky electrode. ZnGa₂O₄, with its wide bandgap of 5.2 eV, is well-suited for high-performance solar-blind photodetection applications. The photodetector demonstrates an impressive responsivity of 280 A/W, indicating its exceptional sensitivity within the solar-blind ultraviolet band. One of the device's notable attributes is its high rejection ratio of 10⁵, which effectively filters out unwanted background signals, enhancing its reliability in various environments. The photodetector also boasts a photodetector responsivity contrast ratio (PDCR) of 10⁷, showcasing its ability to detect even minor changes in incident UV light. Additionally, the device features an outstanding detective of 10¹⁸ Jones, underscoring its capability to precisely detect faint UV signals. It exhibits a fast response time of 80 ms and an ON/OFF ratio of 10⁵, making it suitable for real-time UV sensing applications. The noise-equivalent power (NEP) of 10^-17 W/Hz further highlights its efficiency in detecting low-intensity UV signals. The photodetector also achieves a high forward-to-backward current rejection ratio of 10⁶, ensuring high selectivity. Furthermore, the device maintains an extremely low dark current of approximately 0.1 pA. These findings position the ZnGa₂O₄-based Schottky photodetector as a leading candidate for solar-blind UV detection applications. It offers a compelling combination of sensitivity, selectivity, and operational efficiency, making it a highly promising tool for environments requiring precise and reliable UV detection.

Keywords: wideband gap, solar blind photodetector, MOCVD, zinc gallate

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Authors: Jaber Shurrab, Matloub Hussain

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The rapid pace of changes in the construction industry, technological advancements, and rising costs present tremendous challenges for project managers. Project managers are under severe pressure to minimize the waste, improve the efficiency of the entire operations and the philosophy of ‘lean thinking’ so that ‘more could be achieved with less’ is becoming very popular. Though, lean management has strong roots in manufacturing industry and over the last decade lean philosophy has started gaining attention in the service industry as well. However, little has been known in the context of waste minimization and lean implementation in the construction industry and this paper deals with this important issue. The primary objective of this paper is to propose a conceptual framework for the exploration of appropriate lean techniques applicable to medium and large construction companies and measure their impact on the competitiveness and economic performance of construction companies of United Arab Emirates (UAE). To this end, a comprehensive literature review and interviews with eight project managers of medium and large construction companies of UAE have been conducted. It has been found that competitive, reduce waste and costs are critical to the construction industry. This is an ongoing research in lean management, giving project managers a practical framework for improving the efficiency of their project through various lean techniques. Originality/value: Research significance emphasizes increasing the effectiveness of the construction industry, influences the development of lean construction framework which improves lean construction practices using the lean techniques. This contributes to the effort of applying lean techniques in the construction industry. Limited publications were done in the construction industry mainly in United Arab Emirates (UAE) compared to the lean manufacturing. This research will recommend a systematic approach for the implementing of the anticipated framework within a cyclical look-ahead period and emphasizes the practical implications of the proposed approach.

Keywords: construction, lean, lean manufacturing, waste

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Authors: Luísa P. Cruz-Lopes, Hugo Costa e Silva, Idalina Domingos, José Ferreira, Luís Teixeira de Lemos, Bruno Esteves

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The liquefaction process of cork based tree barks has led to an increase of interest due to its potential innovation in the lumber and wood industries. In this particular study the bark of Quercus cerris (Turkish oak) is used due to its appreciable amount of cork tissue, although of inferior quality when compared to the cork provided by other Quercus trees. This study aims to optimize alkaline catalysis liquefaction conditions, regarding several parameters. To better comprehend the possible chemical characteristics of the bark of Quercus cerris, a complete chemical analysis was performed. The liquefaction process was performed in a double-jacket reactor heated with oil, using glycerol and a mixture of glycerol/ethylene glycol as solvents, potassium hydroxide as a catalyst, and varying the temperature, liquefaction time and granulometry. Due to low liquefaction efficiency resulting from the first experimental procedures a study was made regarding different washing techniques after the filtration process using methanol and methanol/water. The chemical analysis stated that the bark of Quercus cerris is mostly composed by suberin (ca. 30%) and lignin (ca. 24%) as well as insolvent hemicelluloses in hot water (ca. 23%). On the liquefaction stage, the results that led to higher yields were: using a mixture of methanol/ethylene glycol as reagents and a time and temperature of 120 minutes and 200 ºC, respectively. It is concluded that using a granulometry of <80 mesh leads to better results, even if this parameter barely influences the liquefaction efficiency. Regarding the filtration stage, washing the residue with methanol and then distilled water leads to a considerable increase on final liquefaction percentages, which proves that this procedure is effective at liquefying suberin content and lignocellulose fraction.

Keywords: liquefaction, Quercus cerris, polyalcohol liquefaction, temperature

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Authors: Aleksandra Jonas, Aleksandra Gronowska, Maciej Ścigacz, Szymon Jadczak

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Exact Data is a smart tool which helps with meaningful marketing content creation. It helps marketers achieve this by analyzing the text of an advertisement before and after its publication on social media sites like Facebook or Instagram. In our research we focus on four areas of natural language processing (NLP): grammar correction, sentiment analysis, irony detection and advertisement interpretation. Our research has identified a considerable lack of NLP tools for the Polish language, which specifically aid online marketers. In light of this, our research team has set out to create a robust and versatile NLP tool for the Polish language. The primary objective of our research is to develop a tool that can perform a range of language processing tasks in this language, such as sentiment analysis, text classification, text correction and text interpretation. Our team has been working diligently to create a tool that is accurate, reliable, and adaptable to the specific linguistic features of Polish, and that can provide valuable insights for a wide range of marketers needs. In addition to the Polish language version, we are also developing an English version of the tool, which will enable us to expand the reach and impact of our research to a wider audience. Another area of focus in our research involves tackling the challenge of the limited availability of linguistically diverse corpora for non-English languages, which presents a significant barrier in the development of NLP applications. One approach we have been pursuing is the translation of existing English corpora, which would enable us to use the wealth of linguistic resources available in English for other languages. Furthermore, we are looking into other methods, such as gathering language samples from social media platforms. By analyzing the language used in social media posts, we can collect a wide range of data that reflects the unique linguistic characteristics of specific regions and communities, which can then be used to enhance the accuracy and performance of NLP algorithms for non-English languages. In doing so, we hope to broaden the scope and capabilities of NLP applications. Our research focuses on several key NLP techniques including sentiment analysis, text classification, text interpretation and text correction. To ensure that we can achieve the best possible performance for these techniques, we are evaluating and comparing different approaches and strategies for implementing them. We are exploring a range of different methods, including transformers and convolutional neural networks (CNNs), to determine which ones are most effective for different types of NLP tasks. By analyzing the strengths and weaknesses of each approach, we can identify the most effective techniques for specific use cases, and further enhance the performance of our tool. Our research aims to create a tool, which can provide a comprehensive analysis of advertising effectiveness, allowing marketers to identify areas for improvement and optimize their advertising strategies. The results of this study suggest that a smart tool for advertisement analysis can provide valuable insights for businesses seeking to create effective advertising campaigns.

Keywords: NLP, AI, IT, language, marketing, analysis

Procedia PDF Downloads 85