Search results for: multi objective particle swarm optimization (MOPSO)

Authors: Inci Develioglu, Hasan Firat Pulat

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Sustainable development is one of the most important topics in today's world, and it is also an important research topic for geoenvironmental engineering. Dredging process is performed to expand the river and port channel, flood control and accessing harbors. Every year large amount of sediment are dredged for these purposes. Dredged marine soils can be reused as filling materials, road and foundation embankments, construction materials and wildlife habitat developments. In this study, geotechnical engineering properties and compressibility behavior of dredged soil obtained from the Izmir Bay were investigated. The samples with four different organic matter contents were obtained and particle size distributions, consistency limits, pH and specific gravity tests were performed. The consolidation tests were conducted to examine organic matter content (OMC) effects on compressibility behavior of dredged soil. This study has shown that the OMC has an important effect on the engineering properties of dredged soils. The liquid and plastic limits increased with increasing OMC. The lowest specific gravity belonged to sample which has the maximum OMC. The specific gravity values ranged between 2.76 and 2.52. The maximum void ratio difference belongs to sample with the highest OMC (De_11% = 0.38)_. As the organic matter content of the samples increases, the change in the void ratio has also increased. The compression index increases with increasing OMC.

Keywords: compressibility, consolidation, geotechnical properties, organic matter content, dredged soil

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Authors: Adilson Elias Xavier, Otto Corrêa Rotunno Filho, Paulo Canedo De Magalhães

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This paper addresses the issue of automatic parameter estimation in conceptual rainfall-runoff (CRR) models. Due to threshold structures commonly occurring in CRR models, the associated mathematical optimization problems have the significant characteristic of being strongly non-differentiable. In order to face this enormous task, the resolution method proposed adopts a smoothing strategy using a special C∞ differentiable class function. The final estimation solution is obtained by solving a sequence of differentiable subproblems which gradually approach the original conceptual problem. The use of this technique, called Hyperbolic Smoothing Method (HSM), makes possible the application of the most powerful minimization algorithms, and also allows for the main difficulties presented by the original CRR problem to be overcome. A set of computational experiments is presented for the purpose of illustrating both the reliability and the efficiency of the proposed approach.

Keywords: rainfall-runoff models, automatic calibration, hyperbolic smoothing method

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Authors: M. Derraz, M.Farhaoui

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In the water treatment processes, the determination of the optimal dose of the coagulant is an issue of particular concern. Coagulant dosing is correlated to raw water quality which depends on some parameters (turbidity, ph, temperature, conductivity…). The objective of this study is to provide water treatment operators with a tool that enables to predict and replace, sometimes, the manual method (jar testing) used in this plant to predict the optimum coagulant dose. The model is constructed using actual process data for a water treatment plant located in the middle of Morocco (Meknes).

Keywords: coagulation process, aluminum sulfate, model, coagulant dose

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Authors: Sirikwan Ponprateep, Anchalee Tassanakajon, Vichien Rimphanitchayakit

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A Kazal-type serine proteinase inhibitor, SPIPm2, was abundantly expressed in the hemocytes and secreted into shrimp plasma has anti-viral property against white spot syndrome virus (WSSV). To discover the molecular mechanism of antiviral activity, the binding assay showed that SPIPm2 bind to the components of viral particle and shrimp hemocyte. From our previous report, viral target protein of SPIPm2 was identified, namely WSV477 using yeast two-hybrid screening. WSV477 is an early gene product of WSSV and involved in viral propagation. In this study, the co-immunoprecipitation technique and Tandem Mass Spectrometry (LC-MS/MS) was used to identify the target protein of SPIPm2 from shrimp hemocyte. The target protein of SPIPm2 was cyclophilin A. In vertebrate, cyclophilin A or peptidylprolyl isomerase A was reported to be the immune suppressor interacted with cyclosporin A involved in immune defense response. The recombinant cyclophilin A from Penaeus monodon (rPmCypA) was produced in E.coli system and purified using Ni-NTA column to confirm the protein-protein interaction. In vitro pull-down assay showed the interaction between rSPIPm2 and rPmCypA. To study the biological function of these proteins, the expression analysis of immune gene in shrimp defense pathways will be investigated after rPmCypA administration.

Keywords: cyclophilin A, protein-protein interaction, Kazal-type serine proteinase inhibitor, Penaeus monodon

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Authors: S. F. H. Tasfy, N. A. M. Zabidi, M.-S. Shaharun

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Utilization of CO2 as a carbon source to produce valuable chemicals is one of the important ways to reduce the global warming caused by increasing CO2 in the atmosphere. Supported metal catalysts are crucial for the production of clean and renewable fuels and chemicals from the stable CO2 molecules. The catalytic conversion of CO2 into methanol is recently under increased scrutiny as an opportunity to be used as a low-cost carbon source. Therefore, series of the bimetallic Cu/ZnO-based catalyst supported by SBA-15 were synthesized via impregnation technique with different total metal loading and tested in the catalytic hydrogenation of CO2 to methanol. The morphological and textural properties of the synthesized catalysts were determined by transmission electron microscopy (TEM), temperature programmed desorption, reduction, oxidation and pulse chemisorption (TPDRO), and N2-adsorption. The CO2 hydrogenation reaction was performed in microactivity fixed-bed system at 250 °C, 2.25 MPa, and H2/CO2 ratio of 3. Experimental results showed that the catalytic structure and performance was strongly affected by the loading of the active site. Where, the catalytic activity, methanol selectivity as well as the space-time yield increased with increasing the metal loading until it reaches the maximum values at a metal loading of 15 wt% while further addition of metal inhibits the catalytic performance. The higher catalytic activity of 14 % and methanol selectivity of 92 % were obtained over Cu/ZnO-SBA-15 catalyst with total bimetallic loading of 15 wt%. The excellent performance of 15 wt% Cu/ZnO-SBA-15 catalyst is attributed to the presence of well disperses active sites with small particle size, higher Cu surface area, and lower catalytic reducibility.

Keywords: hydrogenation of carbon dioxide, methanol synthesis, metal loading, Cu/ZnO-SBA-15 catalyst

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Authors: Abdelbasset Tounekti, Kamel Boukhalfa, Tathagata Roy Choudhury, Mohamed Soussi, Santanu Banerjee

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The exceptionally glauconite-rich Miocene strata are superbly exposed throughout the front of the nappes zone of northern Tunisia. Each of the glauconitic fine-grained intervals coincide with the peak rise of third order sea-level cycles during the Burdigalian-Langhiantime. These deposits show coarsening- and thickening-upward glauconitic shale and sandstone, recording a shallowing upward progression across offshore-shoreface settings. Petrographic investigation reveals that the glauconite was originated from the alteration of fecal pellets, and lithoclast including feldspar, volcanic particle, and quartz and infillings with intraparticle pores. Mineralogical analysis of both randomly oriented and air-dried, ethylene-glycolate, and heated glauconite pellets show the low intensity of (002) reflection peaks, indicating high iron substitution for aluminum in octahedral sites. Geochemical characterization of the Miocene glauconite reveals a high K2O and variable Fe2O3 (total) content. A combination of layer lattice and divertissement theories explains the origin of glauconite. The formation of glauconite was facilitated by the abundant supply of Fe through contemporaneous volcanism in Algeria and surrounding areas, which accompanied the African-European plate convergence. Therefore, the occurrence of glauconite in the Miocene succession of Tunisia is influenced by the combination of eustacy and volcanism.

Keywords: glauconite, autogenic, volcanism, geochemistry, chamosite, northern Tunisia, miocene

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Authors: Chamnan Kumsap, Somsarit Sinnung, Suriyawate Boonthalarath, Teeranai Srithamarong

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This research paper addresses the GIS analysis approach to the investigation of suitable sites for a vertical takeoff and landing drone. The study manipulated GIS and terrain layers into a proper input before the spatial analysis that included slope, reclassify, classify, and buffer was applied to the individual layers. The output layers were weighted, and multi-criteria analyzed before those patches failing to comply with filtering out criteria were discarded. Field survey for each suitable candidate site was conducted to cross-check the proposed approach with the real world. Conclusion was extracted for the VTOL takeoff and landing sites, and discussion was provided with further study being suggested on the mission simulation of selected takeoff and landing sites.

Keywords: GIS approach, site selection, VTOL, takeoff and landing

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Authors: Maria Da Glória Diniz De Almeida, Andreia M. P. Salgado

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This paper aims to present the creation of a mentoring program to be applied in developing future junior industrial engineers acting professionally. Its objective is to contribute to a better professional performance as engineers. It is a case-study for the RIP region (including the cities of Resende, Itatiaia and Porto Real), which is located in an industrial area in Rio de Janeiro State, in Brazil. As a result, 87% of mentors and mentees approved the program as efficient, based on the initial targets.

Keywords: mentoring program, mentors and mentees, student professional development, young engineers education

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Authors: J. Fernandez, R. Fernandez de Canete, J. Perea-Paizal, J. C. Ramos-Diaz

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The high prevalence of cardiovascular diseases has provoked a raising interest in the development of mathematical models in order to evaluate the cardiovascular function both under physiological and pathological conditions. In this paper, a physical model of the cardiovascular system with intrinsic regulation is presented and implemented by using the object-oriented Modelica simulation software tools.  For this task, a multi-compartmental system previously validated with physiological data has been built, based on the interconnection of cardiovascular elements such as resistances, capacitances and pumping among others, by following an electrohydraulic analogy. The results obtained under both physiological and pathological scenarios provide an easy interpretative key to analyze the hemodynamic behavior of the patient. The described approach represents a valuable tool in the teaching of physiology for graduate medical and nursing students among others.

Keywords: cardiovascular system, MODELICA simulation software, physical modelling, teaching tool

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Authors: Yasser F. Hassan

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Deep learning structure is a branch of machine learning science and greet achievement in research and applications. Cellular neural networks are regarded as array of nonlinear analog processors called cells connected in a way allowing parallel computations. The paper discusses how to use deep learning structure for representing neural cellular automata model. The proposed learning technique in cellular automata model will be examined from structure of deep learning. A deep automata neural cellular system modifies each neuron based on the behavior of the individual and its decision as a result of multi-level deep structure learning. The paper will present the architecture of the model and the results of simulation of approach are given. Results from the implementation enrich deep neural cellular automata system and shed a light on concept formulation of the model and the learning in it.

Keywords: cellular automata, neural cellular automata, deep learning, classification

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Authors: R. H. Hwang, J. H. Park, K. B. Yi

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Nitrous oxide (N2O) is now distinguished as an environmental pollutant. N2O is one of the representative greenhouse gases and N2O is produced by both natural and anthropogenic sources. So, it is very important to reduce N2O. N2O abatement processes are various processes such as HC-SCR, NH3-SCR and decomposition process. Among them, decomposition process is advantageous because it does not use a reducing agent. N2O decomposition is a reaction in which N2O is decomposed into N2 and O2. There are noble metals, transition metal ion-exchanged zeolites, pure and mixed oxides for N2O decomposition catalyst. Among the various catalysts, cobalt-based catalysts derived from hydrotalcites gathered much attention because spinel catalysts having large surface areas and high thermal stabilities. In this study, the effect of promoter and K addition on the activity was compared and analyzed. Co3O4 catalysts for N2O decomposition were prepared by co- precipitation method. Ce and Zr were added during the preparation of the catalyst as promoter with the molar ratio (Ce or Zr) / Co = 0.05. In addition, 1 wt% K2CO3 was doped to the prepared catalyst with impregnation method to investigate the effect of K on the catalyst performance. Characterizations of catalysts were carried out with SEM, BET, XRD, XPS and H2-TPR. The catalytic activity tests were carried out at a GHSV of 45,000 h-1 and a temperature range of 250 ~ 375 ℃. The Co3O4 catalysts showed a spinel crystal phase, and the addition of the promoter increased the specific surface area and reduced the particle and crystal size. It was exhibited that the doping of K improves the catalytic activity by increasing the concentration of Co2+ in the catalyst which is an active site for catalytic reaction. As a result, the K-doped catalyst showed higher activity than the promoter added. Also, it was found through experiments that Co2+ concentration and reduction temperature greatly affect the reactivity.

Keywords: Co₃O4, K-doped, N₂O decomposition, promoter

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Authors: Ijaz Ahmed, Anwar Zada, Muhammad Waqas, M. U. Ashraf

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We demonstrate the performance of a very efficient tagger applies on hadronically decaying top quark pairs as signal based on deep neural network algorithms and compares with the QCD multi-jet background events. A significant enhancement of performance in boosted top quark events is observed with our limited computing resources. We also compare modern machine learning approaches and perform a multivariate analysis of boosted top-pair as well as single top quark production through weak interaction at √s = 14 TeV proton-proton Collider. The most relevant known background processes are incorporated. Through the techniques of Boosted Decision Tree (BDT), likelihood and Multlayer Perceptron (MLP) the analysis is trained to observe the performance in comparison with the conventional cut based and count approach

Keywords: top tagger, multivariate, deep learning, LHC, single top

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Authors: Soumava Boral, Sanjay K. Chaturvedi, Ian Howard, Kristoffer McKee, V. N. A. Naikan

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Due to strict regulations from government to enhance the possibilities of sustainability practices in industries, and noting the advances in sustainable manufacturing practices, it is necessary that the associated processes are also sustainable. Maintenance of large scale and complex machines is a pivotal task to maintain the uninterrupted flow of manufacturing processes. Appropriate maintenance practices can prolong the lifetime of machines, and prevent associated breakdowns, which subsequently reduces different cost heads. Selection of the best maintenance strategies for such machines are considered as a burdensome task, as they require the consideration of multiple technical criteria, complex mathematical calculations, previous fault data, maintenance records, etc. In the era of the fourth industrial revolution, organizations are rapidly changing their way of business, and they are giving their utmost importance to sensor technologies, artificial intelligence, data analytics, automations, etc. In this work, the effectiveness of several maintenance strategies (e.g., preventive, failure-based, reliability centered, condition based, total productive maintenance, etc.) related to a large scale and complex gearbox, operating in a steel processing plant is evaluated in terms of economic, social, environmental and technical criteria. As it is not possible to obtain/describe some criteria by exact numerical values, these criteria are evaluated linguistically by cross-functional experts. Fuzzy sets are potential soft-computing technique, which has been useful to deal with linguistic data and to provide inferences in many complex situations. To prioritize different maintenance practices based on the identified sustainable criteria, multi-criteria decision making (MCDM) approaches can be considered as potential tools. Multi-Attributive Ideal Real Comparative Analysis (MAIRCA) is a recent addition in the MCDM family and has proven its superiority over some well-known MCDM approaches, like TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) and ELECTRE (ELimination Et Choix Traduisant la REalité). It has a simple but robust mathematical approach, which is easy to comprehend. On the other side, due to some inherent drawbacks of Intuitionistic Fuzzy Sets (IFS) and Pythagorean Fuzzy Sets (PFS), recently, the use of Fermatean Fuzzy Sets (FFSs) has been proposed. In this work, we propose the novel concept of FF-MAIRCA. We obtain the weights of the criteria by experts’ evaluation and use them to prioritize the different maintenance practices according to their suitability by FF-MAIRCA approach. Finally, a sensitivity analysis is carried out to highlight the robustness of the approach.

Keywords: Fermatean fuzzy sets, Fermatean fuzzy MAIRCA, maintenance strategy selection, sustainable manufacturing, MCDM

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Authors: Shweta Kumari, Parmjit S. Panesar, Manab B. Bera

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The hydrolysis of lactose using β-galactosidase is one of the most promising biotechnological applications, which has wide range of potential applications in food processing industries. However, due to intracellular location of the yeast enzyme, and expensive extraction methods, the industrial applications of enzymatic hydrolysis processes are being hampered. The use of permeabilization technique can help to overcome the problems associated with enzyme extraction and purification of yeast cells and to develop the economically viable process for the utilization of whole cell biocatalysts in food industries. In the present investigation, standardization of permeabilization process of novel yeast isolate was carried out using a statistical model approach known as Response Surface Methodology (RSM) to achieve maximal b-galactosidase activity. The optimum operating conditions for permeabilization process for optimal β-galactosidase activity obtained by RSM were 1:1 ratio of toluene (25%, v/v) and ethanol (50%, v/v), 25.0 oC temperature and treatment time of 12 min, which displayed enzyme activity of 1.71 IU /mg DW.

Keywords: β-galactosidase, optimization, permeabilization, response surface methodology, yeast

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Authors: Domingo Planes Meseguer, Raffaele Danilo Esposito, Maria Del Pilar Dorado Rodriguez

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Monthly quality control checks for a Radiation Therapy Linac may be performed is a simple and efficient way once they have been standardized and protocolized. On the other hand this checks, in spite of being imperatives, require a not negligible execution times in terms of machine time and operators time. Besides it must be taken into account the amount of disposable material which may be needed together with the use of commercial software for their performing. With the aim of optimizing and standardizing mechanical-geometric checks and multi leaves collimator checks, we decided to implement a protocol which makes use of the Electronic Portal Imaging Device (EPID) available on our Linacs. The user is step by step guided by the software during the whole procedure. Acquired images are automatically analyzed by our programs all of them written using only free software.

Keywords: quality control checks, linac, radiation oncology, medical physics, free software

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Authors: Abdallah H. Ramini, Alwathiqbellah I. Ibrahim, Mohammad I. Younis

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We investigate experimentally and theoretically the dynamics of a capacitive resonator under mixed frequency excitation of two AC harmonic signals. The resonator is composed of a proof mass suspended by two cantilever beams. Experimental measurements are conducted using a laser Doppler Vibrometer to reveal the interesting dynamics of the system when subjected to two-source excitation. A nonlinear single-degree-of-freedom model is used for the theoretical investigation. The results reveal combination resonances of additive and subtractive type, which are shown to be promising to increase the bandwidth of the resonator near primary resonance frequency. Our results also demonstrate the ability to shift the combination resonances to much lower or much higher frequency ranges. We also demonstrate the dynamic pull-in instability under mixed frequency excitation.

Keywords: electrostatically actuated resonator, multi-frequency excitation, nonlinear dynamics, AC harmonic signals

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Authors: Ngoc Minh Quynh Pham, Quan V. Vuong, Michael C. Bowyer, Christopher J. Scarlett

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Tuckeroo (Cupaniopsis anacardioides) is an Australian native plant and is grown in the coastal regions in New South Wales, Queensland and Northern Australia. Its fruits have been eaten by birds; however there is no information on phytochemical and antioxidant capacity of these fruits. This study aimed to determine the phenolic compounds (TPC), flavonoids (TFC), proanthocyanidins (TPro) and antioxidant capacity in the whole or different parts of tuckeroo fruit including skin, flesh and seed. Whole and partly tuckeroo fruits were collected and immediately freeze dried to constant weight and then ground to small particle sizes (<1mm mesh). Samples were extracted in 50% methanol using an ultrasonic bath set at temperature 40 °C for 30 minutes. TPC, TFC, TPro and antioxidant capacity were measured by spectrophotometric analysis. The results showed that the whole fruits contained 106.23 mg GAE/g of TPC, 67.67 mg CAE/g of TFC and 56.74 mg CAE/g of TPro. These fruits also possessed high antioxidant capacity (DPPH: 263.78 mg TroE/g, ABTS: 346.98 mg TroE/g, CUPRAC: 370.12 mg TroE/g and FRAP: 176.30 mg TroE/g), revealing that these fruits are rich source of antioxidants. The results also showed that distribution of the antioxidants was varied in different parts of the fruits. Skin had the highest levels of TPC, TFC, and TPro as well as antioxidant properties, followed by the seed and flesh had the lowest levels of phenolic compounds and antioxidant capacity. Of note, levels of phenolic compounds and antioxidant capacity of the skin were significantly higher than those of the whole fruits. Therefore, the skin of tuckeroo fruits is recommended as a starting material for extraction and purification of phenolic compounds as potential antioxidants for further utilisation in the food and pharmaceutical industries.

Keywords: antioxidant capacity, Cupaniopsis anacardioides, phenolic compounds, tuckeroo fruit

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Authors: Tiffany Baetens, Sophie Halliez, Luc Buée, Emiliano Pallecchi, Vincent Thomy, Steve Arscott

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We demonstrate here a viable stretchable bio-microelectromechanical systems (BioMEMS) technology for use with biological studies concerned with the effect of high mechanical strains on living cells. An example of this is traumatic brain injury (TBI) where neurons are damaged with physical force to the brain during, e.g., accidents and sports. Robust, miniaturized integrated systems are needed by biologists to be able to study the effect of TBI on neuron cells in vitro. The major challenges in this area are (i) to develop micro, and nanofabrication processes which are based on stretchable substrates and to (ii) create systems which are robust and performant at very high mechanical strain values—sometimes as high as 100%. At the time of writing, such processes and systems were rapidly evolving subject of research and development. The BioMEMS which we present here is composed of an elastomer substrate (low Young’s modulus ~1 MPa) onto which is patterned robust electrodes and insulators. The patterning of the thin films is achieved using standard photolithography techniques directly on the elastomer substrate—thus making the process generic and applicable to many materials’ in based systems. The chosen elastomer used is commercial ‘Sylgard 184’ polydimethylsiloxane (PDMS). It is spin-coated onto a silicon wafer. Multistep ultra-violet based photolithography involving commercial photoresists are then used to pattern robust thin film metallic electrodes (chromium/gold) and insulating layers (parylene) on the top of the PDMS substrate. The thin film metals are deposited using thermal evaporation and shaped using lift-off techniques The BioMEMS has been characterized mechanically using an in-house strain-applicator tool. The system is composed of 12 electrodes with one reference electrode transversally-orientated to the uniaxial longitudinal straining of the system. The electrical resistance of the electrodes is observed to remain very stable with applied strain—with a resistivity approaching that of evaporated gold—up to an interline strain of ~50%. The mechanical characterization revealed some interesting original properties of such stretchable BioMEMS. For example, a Poisson effect induced electrical ‘self-healing’ of cracking was identified. Biocompatibility of the commercial photoresist has been studied and is conclusive. We will present the results of the BioMEMS, which has also characterized living cells with a commercial Multi Electrode Array (MEA) characterization tool (Multi Channel Systems, USA). The BioMEMS enables the cells to be strained up to 50% and then characterized electrically and optically.

Keywords: BioMEMS, elastomer, electrical impedance measurements of living cells, high mechanical strain, microfabrication, stretchable systems, thin films, traumatic brain injury

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Authors: Maria Amodeo, Pasquale Arpaia, Marco Buzio, Vincenzo Di Capua, Francesco Donnarumma

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Different deep neural network architectures have been compared and tested to predict magnetic hysteresis in the context of pulsed electromagnets for experimental physics applications. Modelling quasi-static or dynamic major and especially minor hysteresis loops is one of the most challenging topics for computational magnetism. Recent attempts at mathematical prediction in this context using Preisach models could not attain better than percent-level accuracy. Hence, this work explores neural network approaches and shows that the architecture that best fits the measured magnetic field behaviour, including the effects of hysteresis and eddy currents, is the nonlinear autoregressive exogenous neural network (NARX) model. This architecture aims to achieve a relative RMSE of the order of a few 100 ppm for complex magnetic field cycling, including arbitrary sequences of pseudo-random high field and low field cycles. The NARX-based architecture is compared with the state-of-the-art, showing better performance than the classical operator-based and differential models, and is tested on a reference quadrupole magnetic lens used for CERN particle beams, chosen as a case study. The training and test datasets are a representative example of real-world magnet operation; this makes the good result obtained very promising for future applications in this context.

Keywords: deep neural network, magnetic modelling, measurement and empirical software engineering, NARX

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Authors: Liu Lin Xin

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With the increasing complexity of systems, log parsing and anomaly detection have become crucial for maintaining system stability. However, traditional methods often struggle with adaptability and accuracy, especially when dealing with rapidly evolving log content and unfamiliar domains. To address these challenges, this paper proposes approach that integrates Retrieval Augmented Generation (RAG) technology with Prompt Engineering for Large Language Models, applied specifically in LogPrompt. This approach enables dynamic log parsing and intelligent anomaly detection by combining real-time information retrieval with prompt optimization. The proposed method significantly enhances the adaptability of log analysis and improves the interpretability of results. Experimental results on several public datasets demonstrate the method's superior performance, particularly in scenarios lacking training data, where it significantly outperforms traditional methods. This paper introduces a novel technical pathway for log parsing and anomaly detection, showcasing the substantial theoretical value and practical potential.

Keywords: log parsing, anomaly detection, RAG, prompt engineering, LLMs

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Authors: S. P. Koko, K. Kusakana, H. J. Vermaak

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Standalone micro-hydrokinetic river (MHR) system is one of the promising technologies to be used for remote rural electrification. It simply requires the flow of water instead of elevation or head, leading to expensive civil works. This paper demonstrates an economic benefit offered by a standalone MHR system when compared to the commonly used standalone systems such as solar, wind and diesel generator (DG) at the selected study site in Kwazulu Natal. Wind speed and solar radiation data of the selected rural site have been taken from national aeronautics and space administration (NASA) surface meteorology database. The hybrid optimization model for electric renewable (HOMER) software was used to determine the most feasible solution when using MHR, solar, wind or DG system to supply 5 rural houses. MHR system proved to be the best cost-effective option to consider at the study site due to its low cost of energy (COE) and low net present cost (NPC).

Keywords: economic analysis, micro-hydrokinetic, rural-electrification, cost of energy (COE), net present cost (NPC)

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Authors: Elena Puica

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This paper aims to investigate the impact of Supply Chain Management (SCM) on the environment by applying a Machine Learning model while pointing out the efficiency of the technology used. The Machine Learning model was used to derive the efficiency and optimization of technology used in SCM and the environmental impact of SCM processes. The model applied is a predictive classification model and was trained firstly to determine which stage of the SCM has more outputs and secondly to demonstrate the efficiency of using advanced technology in SCM instead of recuring to traditional SCM. The outputs are the emissions generated in the environment, the consumption from different steps in the life cycle, the resulting pollutants/wastes emitted, and all the releases to air, land, and water. This manuscript presents an innovative approach to applying advanced technology in SCM and simultaneously studies the efficiency of technology and the SCM's impact on the environment. Identifying the conceptual relationships between SCM practices and their impact on the environment is a new contribution to the research. The authors can take a forward step in developing recent studies in SCM and its effects on the environment by applying technology.

Keywords: machine-learning model in SCM, SCM processes, SCM and the environmental impact, technology in SCM

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Authors: Jihene Jebeniani

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We proposed an approach combining both the techniques of experimental economy and the flexibility of discrete choice models in order to test the loss aversion. Our main objective was to test the loss aversion of the Cumulative Prospect Theory (CPT). We developed an experimental laboratory in the context of the financial investments that aimed to analyze the attitude towards the risk of the investors. The study uses the lotteries and is basing on econometric modeling. The estimated model was the ordered probit.

Keywords: risk aversion, behavioral finance, experimental economic, lotteries, cumulative prospect theory

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Authors: Andrés I. Ávila, Patricia Aros, César San Martín, Elizabeth Kehr, Yovana Leal

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Planning models for fresh products is a very useful tool for improving the net profits. To get an efficient supply chain model, several functions should be considered to get a complete simulation of several operational units. We consider a linear programming model to help farmers to decide if it is convenient to choose what area should be planted for three kinds of export fruits considering their future investment. We consider area, investment, water, productivity minimal unit, and harvest restrictions to develop a monthly based model to compute the average income in five years. Also, conditions on the field as area, water availability, and initial investment are required. Using the Chilean costs and dollar-peso exchange rate, we can simulate several scenarios to understand the possible risks associated to this market. Also, this tool help to support decisions for government and individual farmers.

Keywords: mixed integer problem, fresh fruit production, support decision model, agricultural and biosystems engineering

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Authors: M. Eickermann, M. K. Class, J. Junk

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Winter oilseed rape, Brassica napus L., is characterized by a high number of herbicide applications. Therefore, its cultivation can lead to massive contamination of ground water and soil by herbicide and their metabolites. A multi-side long-term field experiment (EFFO, Efficient crop rotation) was set-up in Luxembourg to quantify these effects. Based on soil sampling and laboratory analysis, preliminary results showed reduced dehydrogenase activities of several soil organisms due to herbicide treatments. This effect is highly depending on the soil type. Relation between the dehydrogenase activity and the amount of microbial carbon showed higher variability on the test side with loamy Brown Earth, based on Bunter than on those with sandy-loamy Brown Earth, based on calciferous Sandstone.

Keywords: cropping system, dehydrogenase activity, herbicides, mechanical weed control, oilseed rape

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Authors: S. M. Giripunje, Shikha Jindal

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Zinc sulphide (ZnS) quantum dots (QDs) were synthesized successfully via simple sonochemical method. X-ray diffraction (XRD), scanning electron microscopy (SEM) and high resolution transmission electron microscopy (HRTEM) analysis revealed the average size of QDs of the order of 3.7 nm. The band gap of the QDs was tuned to 5.2 eV by optimizing the synthesis parameters. UV-Vis absorption spectra of ZnS QD confirm the quantum confinement effect. Fourier transform infrared (FTIR) analysis confirmed the formation of single phase ZnS QDs. To fabricate the diode, blend of ZnS QDs and P3HT was prepared and the heterojunction of PEDOT:PSS and the blend was formed by spin coating on indium tin oxide (ITO) coated glass substrate. The diode behaviour of the heterojunction was analysed, wherein the ideality factor was found to be 2.53 with turn on voltage 0.75 V and the barrier height was found to be 1.429 eV. ZnS-Graphene QDs nanocomposite was characterised for the surface morphological study. It was found that the synthesized ZnS QDs appear as quasi spherical particles on the graphene sheets. The average particle size of ZnS-graphene nanocomposite QDs was found to be 8.4 nm. From voltage-current characteristics of ZnS-graphene nanocomposites, it is observed that the conductivity of the composite increases by 10⁴ times the conductivity of ZnS QDs. Thus the addition of graphene QDs in ZnS QDs enhances the mobility of the charge carriers in the composite material. Thus, the graphene QDs, with high specific area for a large interface, high mobility and tunable band gap, show a great potential as an electron-acceptors in photovoltaic devices.

Keywords: graphene, heterojunction, quantum confinement effect, quantum dots(QDs), zinc sulphide(ZnS)

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Authors: Gomti Devi

Abstract:

Photoisomerization has been attracting to researchers due to its wide range of applications in optical switches, polymeric chains, liquid-crystalline systems and bilayer membranes etc. Azobenzene is a photochromic molecule which exhibits a reversible isomerisation process between its trans and cis isomers of different stability. An investigation has been conducted of the effects of temperature on intensity and position of Raman band of N=N, C-N stretching modes of Azobenzene (AZBN). It was found that the N=N stretching mode of Raman band shape shifts to lower frequency region with the increase in temperature. The Raman intensity was also decreased with the increase of temperature. The change in bandwidth with the increase in temperature has been studied. The FTIR spectrum of the molecule is recorded so as to complement the Raman spectra. In order to investigate the possibility of undergoing dimerization and trimerization as well as the stability of this molecule, ab initio calculation for geometry optimization and vibrational wavenumber calculation have been performed. Theoretically calculated values are found in good agreement with the experimental results.

Keywords: azobenzene, temperature, ab-initio, frequency

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Authors: Shaukat Ali, Kanber Sedef, Mustafa Yilmaz

Abstract:

This work analyzes a finger mechanism for a prosthetic hand that will help in improving the living standards of people who have lost their hands for a variety of reasons. The finger mechanism is single degree of freedom and hence has advantages such as compact size, reduced mass and less energy consumption. The proposed finger mechanism is a six bar linkage actuated by a single motor. The kinematic, static and dynamic analyses have been done by using the conventional methods of mechanism analysis. The kinematic results present the motion of the proposed finger mechanism and location of the fingertip. The static and dynamic analyses provide the useful information about the gripping force at the fingertip for various configurations and the selection of motor that will move the finger over its range of configuration. This single motor finger mechanism is simple and resembles the human finger’s motion suitable for grasping operation. This study can be used in the optimization of geometrical parameters of the proposed mechanism to obtain the desired configurations with minimum torque and enhanced griping.

Keywords: dynamics, finger mechanism, grasping, kinematics

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Authors: Muhammad Yousaf Ali, Shahid Mansoor, Javeria Qazi, Imran Amin, Musarrat Shaheen

Abstract:

Cotton leaf curl disease (CLCuD) is the major threat to the cotton crop and is transmitted by whitefly (Bemisia tabaci). Since multiple begomoviruses and associated satellites are involved in CLCuD, approaches based on the concept of broad-spectrum resistance are essential for effective disease control. Gro-El and G5 are two proteins from whitefly endosymbiont and M13 bacteriophage origin, respectively. Gro-El encapsulates the virus particle when it enters the whitefly and protects the virus from the immune system of the whitefly as well as prevents viral expression in it. This characteristic of Gro-El can be exploited to get resistance against viruses if expressed in plants. G5 is a single-stranded DNA binding protein, expression of which in transgenic plants will stop viral expression on its binding with ssDNA. The use of tissue-specific promoters is more efficient than constitutive promoters. Transgenics of Nicotiana benthamiana for Gro-El under constitutive promoter and Gro-El under phloem specific promoter were made. In comparison to non-transgenic plants, transgenic plants with Gro-El under NSP promoter showed promising results when challenged against cotton leaf curl Multan virus (CLCuMuV) along with cotton leaf curl Multan beta satellite (CLCuMB), cotton leaf curl Khokhran virus (CLCuKoV) along with cotton leaf curl Multan beta satellite (CLCuMB) and Pedilenthus leaf curl virus (PedLCV) along with Tobacco leaf curl beta satellite (TbLCB).

Keywords: cotton leaf curl disease, whitefly, endosymbionts, transgenic, resistance

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Authors: T. J. Jamaleddine

Abstract:

Purge columns or degasser vessels are widely used in the polyolefin process for removing trapped hydrocarbons and in-excess catalyst residues from the polymer particles. A uniform distribution of purged gases coupled with a plug-flow characteristic inside the column system is desirable to obtain optimum desorption characteristics of trapped hydrocarbon and catalyst residues. Computational Fluid Dynamics (CFD) approach is a promising tool for design optimization of these vessels. The success of this approach is profoundly dependent on the solution strategy and the choice of geometrical layout at the vessel outlet. Filling the column with solids and initially solving for the solids flow minimized numerical diffusion substantially. Adopting a cylindrical configuration at the vessel outlet resulted in less numerical instability and resembled the hydrodynamics flow of solids in the hopper segment reasonably well.

Keywords: CFD, degasser vessel, gas-solids flow, gas purging, purge column, species transport

Procedia PDF Downloads 122