Search results for: gaussian mixture models

Authors: Rizwan Latif, Syed Adnan Qasim, Muzaffar Ali

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Fuel direct injection represents one of the key aspects in the development of the diesel engines, the idea of controlling the auto-ignition and the consequent combustion of a liquid spray injected in a reacting atmosphere during a time scale of few milliseconds has been a challenging task for the engine community and pushed forward to a massive research in this field. The quality of the air-fuel mixture defines the combustion efficiency, and therefore the engine efficiency. A droplet interaction in dense as well as thin portion of the spray receives equal importance as other parameters in spray structure. Usually, these are modeled along with breakup process and analyzed alike. In this paper, droplet interaction is modeled and simulated for high torque low speed scenario. Droplet interactions may further be subdivided into droplet collision and coalescence, spray wall impingement, droplets drag, etc. Droplet collisions may occur in almost all spray applications, but especially in diesel like conditions such as high pressure sprays as utilized in combustion engines. These collisions have a strong influence on the mean droplet size and its spatial distribution and can, therefore, affect sub-processes of spray combustion such as mass, momentum and energy transfer between gas and droplets. Similarly, for high-pressure injection systems spray wall impingement is an inherent sub-process of mixture formation. However, its influence on combustion is in-explicit.

Keywords: droplet collision, coalescence, low speed, diesel fuel

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Authors: Anmar Dulaimi, Hassan Al Nageim, Felicite Ruddock, Linda Seton

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Cold bituminous asphalt mixture (CBEM) provide a sustainable, cost effective and energy efficiency alternative to traditional hot mixtures. However, these mixtures have a comparatively low initial strength and as it is considered as evolutionary materials, mainly in the early life where the initial cohesion is low and builds up slowly. On the other hand, asphalt concrete is, by far, the most common mixtures in use as binder course and base in road pavement in the UK having a continuous grade offer a good aggregate interlock results in this material having very good load-spreading properties as well as a high resistance to permanent deformation. This study aims at developing a novel fast curing cold asphalt concrete binder course mixtures by using Ordinary Portland Cement (OPC) as a replacement to conventional mineral filler (0%-100%) while new by-product material (LJMU-A2) was used as a supplementary cementitious material. With this purpose, cold asphalt concrete binder course mixtures with cationic emulsions were studied by means of stiffness modulus whereas water sensitivity was approved by assessing the stiffness modulus ratio before and after sample conditioning. The results indicate that a substantial enhancement in the stiffness modulus and a considerable improvement of water sensitivity resistance by adding of LJMU-A2 to the cold asphalt mixtures as a supplementary cementitious material. Moreover, the addition of LJMU-A2 to those mixtures leads to stiffness modulus after 2- day curing comparable to those obtained with Portland cement after 7-day curing.

Keywords: cold mix asphalt, binder course, cement, stiffness modulus, water sensitivity

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Authors: Paulo Castro, J. R. Croca, M. Gatta, R. Moreira

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Owing to the development of quantum mechanics, we will contextualize the foundations of the theory on the Fourier analysis framework, thus stating the unavoidable philosophical conclusions drawn by Niels Bohr. We will then introduce an alternative way of describing the undulatory aspects of quantum entities by using gaussian Morlet wavelets. The description has its roots in de Broglie's realistic program for quantum physics. It so happens that using wavelets it is possible to formulate a more general set of uncertainty relations. A set from which it is possible to theoretically describe both ends of the behavioral spectrum in reality: the indeterministic quantum trajectorial foam and the perfectly drawn Newtonian trajectories.

Keywords: philosophy of quantum mechanics, quantum realism, morlet wavelets, uncertainty relations, determinism

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Authors: Waqas Ali, Nadeem Majeed

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Requirement engineering is an essence of software development life cycle. The more time we spend on requirement engineering, higher the probability of success. Effective requirement engineering ensures and predicts successful software product. This paper presents the adaptation of requirement engineering practices in small and medium size companies of Pakistan. The study is conducted by questionnaires to show how much of requirement engineering models and practices are followed in Pakistan.

Keywords: requirement engineering, Pakistan, models, practices, organizations

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Authors: Natália Botica, Luís Luís, Paulo Bernardes

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The Côa Valley, listed World Heritage since 1998, presents more than 1300 open-air engraved rock panels. The Archaeological Park of the Côa Valley recorded the rock art motifs, testing various techniques based on direct tracing processes on the rock, using natural and artificial lighting. In this work, integrated in the "Open Access Rock Art Repository" (RARAA) project, we present the methodology adopted for the vectorial drawing of the rock art motifs based on orthophotos taken from the photogrammetric survey and 3D models of the rocks. We also present the information system designed to integrate the vector drawing and the characterization data of the motifs, as well as the open access sharing, in order to promote their reuse in multiple areas. The 3D models themselves constitute a very detailed record, ensuring the digital preservation of the rock and iconography. Thus, even if a rock or motif disappears, it can continue to be studied and even recreated.

Keywords: rock art, archaeology, iron age, 3D models

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Authors: Beata Kaczmarek-Szczepańska, Lidia Zasada

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Marine collagens such as fish skin, bone, cartilage, and scales, including both marine vertebrates and invertebrates sources, are more bioavailable compared to bovine or porcine collagen and have a higher absorption capability and more rapid bloodstream circulation due to their low molecular weight and small particle size. Fish skin may be used as a source of bioactive compounds. The advantage is that fish skin is a by-product of the food industry. The subject of the study is a lyophilizate consisting of a mixture of compounds from the group of glycosaminoglycans and collagen obtained as a result of processing fish skins. Bioactive compounds may find biomedical use in the production of dressing materials for wounds or in tissue engineering for the production of scaffolds for cells. Collagen and glycosaminoglycans were isolated from Salmo salar skin. The final mixture was obtained by the freeze-drying method. In the obtained lyophilizate, the content of amino acids was studied as well as the presence of polysaccharides. The studies showed the presence of glycine, proline, and hydroxyproline, which are the main amino acids of collagen. The HPLC analysis showed the presence of glucose which is a product of polysaccharides hydrolyzation and then reduction of glucuronic acid. It may be assumed that the lyophilizate contains both collagen and polysaccharide, which is probably hyaluronic acid. Acknowledgment: This work was carried out as a result of research project no. TANGO-V-A/0020/2021 financed by the National Centre for Research and Development.

Keywords: collagen, glycosaminoglycans, bioactive compounds, fish skin

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Authors: H. Jawan

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This review describes the models of environmental-related crack propagation of aluminum alloys (7xxx) during the last few decades. Acknowledge on effects of different factors on the susceptibility to SCC permits to propose valuable mechanisms on crack advancement. The reliable mechanism of cracking give a possibility to propose the optimum chemical composition and thermal treatment conditions resulting in microstructure the most suitable for real environmental condition and stress state.

Keywords: microstructure, environmental, propagation, mechanism

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Authors: Masood Varshosaz, Kamyar Hasanpour

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In recent years, we have seen growing interest in recognizing humans in drone images for post-disaster search and rescue operations. Deep learning algorithms have shown great promise in this area, but they often require large amounts of labeled data to train the models. To keep the data acquisition cost low, augmentation techniques can be used to create additional data from existing images. There are many techniques of such that can help generate variations of an original image to improve the performance of deep learning algorithms. While data augmentation is potentially assumed to improve the accuracy and robustness of the models, it is important to ensure that the performance gains are not outweighed by the additional computational cost or complexity of implementing the techniques. To this end, it is important to evaluate the impact of data augmentation on the performance of the deep learning models. In this paper, we evaluated the most currently available 2D data augmentation techniques on a standard convolutional network which was trained for recognizing humans in drone images. The techniques include rotation, scaling, random cropping, flipping, shifting, and their combination. The results showed that the augmented models perform 1-3% better compared to a base network. However, as the augmented images only contain the human parts already visible in the original images, a new data augmentation approach is needed to include the invisible parts of the human body. Thus, we suggest a new method that employs simulated 3D human models to generate new data for training the network.

Keywords: human recognition, deep learning, drones, disaster mitigation

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Authors: Shalini Menon, K. Girish Kumar

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Norepinephrine (NE) is one of the naturally occurring catecholamines which act both as a neurotransmitter and a hormone. Catecholamine levels are used for the diagnosis and regulation of phaeochromocytoma, a neuroendocrine tumor of the adrenal medulla. The development of simple, rapid and cost-effective sensors for NE still remains a great challenge. Herein, a dual-channel sensor has been developed for the determination of NE. A mixture of AgNO₃, NaOH, NH₃.H₂O and cetrimonium bromide in appropriate concentrations was taken as the working solution. To the thoroughly vortexed mixture, an appropriate volume of NE solution was added. After a particular time, the fluorescence and absorbance were measured. Fluorescence measurements were made by exciting at a wavelength of 400 nm. A dual-channel optical sensor has been developed for the colorimetric as well as the fluorimetric determination of NE. Metal enhanced fluorescence property of nanoparticles forms the basis of the fluorimetric detection of this assay, whereas the appearance of brown color in the presence of NE leads to colorimetric detection. Wide linear ranges and sub-micromolar detection limits were obtained using both the techniques. Moreover, the colorimetric approach was applied for the determination of NE in synthetic blood serum and the results obtained were compared with the classic high-performance liquid chromatography (HPLC) method. Recoveries between 97% and 104% were obtained using the proposed method. Based on five replicate measurements, relative standard deviation (RSD) for NE determination in the examined synthetic blood serum was found to be 2.3%. This indicates the reliability of the proposed sensor for real sample analysis.

Keywords: norepinephrine, colorimetry, fluorescence, silver nanoparticles

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Authors: Saghar Heidari

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In this paper, we study the valuation problem of European continuous-installment options under Markov-modulated models with a partial differential equation approach. Due to the opportunity for continuing or stopping to pay installments, the valuation problem under regime-switching models can be formulated as coupled partial differential equations (CPDE) with free boundary features. To value the installment options, we express the truncated CPDE as a linear complementarity problem (LCP), then a finite element method is proposed to solve the resulted variational inequality. Under some appropriate assumptions, we establish the stability of the method and illustrate some numerical results to examine the rate of convergence and accuracy of the proposed method for the pricing problem under the regime-switching model.

Keywords: continuous-installment option, European option, regime-switching model, finite element method

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Authors: Anne Elain, Magali Le Fellic

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Halogenated Aromatic Compounds (HAC) are major organic pollutants that are detected in several environmental compartments as a result of their widespread use as solvents, pesticides and other industrial chemicals. The degradation of HAC simultaneously at low temperature and under saline conditions would be useful for remediation of polluted sites. Hence, microbial processes based on the metabolic activities of anaerobic bacteria are especially attractive from an economic and environmental point of view. Metabolites are generally less toxic, less likely to bioaccumulate and more susceptible for further degradation. Studies on biological reductive dehalogenation have largely been restricted to chlorinated compounds while relatively few have focussed on other HAC i.e., fluorinated, brominated or iodinated compounds. The objectives of the present work were to investigate the biodegradation of a mixture of triiodoaromatic molecules in industrial wastewater by an enriched bacterial consortium. Biodegradation of the mixture was studied during batch experiments in an anaerobic reactor. The degree of mineralization and recovery of halogen were monitored by HPLC-UV, TOC analysis and potentiometric titration. Providing ethanol as an electron donor was found to stimulate anaerobic reductive dehalogenation of HAC with a deiodination rate up to 12.4 mg.L-1 per day. Sodium chloride even at high concentration (10 mM) was found to have no influence on the degradation rates nor on the microbial viability. An analysis of the 16S rDNA (MicroSeq®) revealed that at least 6 bacteria were predominant in the enrichment, including Pseudomonas aeruginosa, Pseudomonas monteilii, Kocuria rhizophila, Ochrobacterium anthropi, Ralstonia pickettii and Rhizobium rhizogenes.

Keywords: halogenated aromatics, anaerobic biodegradation, deiodination, bacterial consortium

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Authors: Patrick Ho

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Bloodstain Pattern Analysis (BPA) forensic evidence can be complex, requiring effective courtroom presentation to ensure clear and comprehensive understanding of the analyst’s findings. BPA witness statements can often involve reference to spatial information (such as location of rooms, objects, walls) which, when coupled with classified blood patterns, may illustrate the reconstructed movements of suspects and injured parties. However, it may be difficult to communicate this information through photography alone, despite this remaining the UK’s established method for presenting BPA evidence. Through an academic-police partnership between the University of Warwick and West Midlands Police (WMP), an integrated 3D scanning and HDR photography workflow for BPA was developed. Homicide scenes were laser scanned and, after processing, the 3D models were utilised in the BPA peer-review process. The same 3D models were made available for court but were not always utilised. This workflow has improved the ease of presentation for analysts and provided 3D scene models that assist with the investigation. However, the effects of incorporating 3D scene models in judicial processes may need to be studied before they are adopted more widely. 3D models from a simulated crime scene and West Midlands Police cases approved for conference disclosure are presented. We describe how the workflow was developed and integrated into established practices at WMP, including peer-review processes and witness statement delivery in court, and explain the impact the work has had on the Criminal Justice System in the West Midlands.

Keywords: bloodstain pattern analysis, forensic science, criminal justice, 3D scanning

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Authors: Junjie Zhong, Kai Hong, Lei Wang

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Passage Retrieval (PR) plays an important role in many Natural Language Processing (NLP) tasks. Traditional efficient retrieval models relying on exact term-matching, such as TF-IDF or BM25, have nowadays been exceeded by pre-trained language models which match by semantics. Though they gain effectiveness, deep language models often require large memory as well as time cost. To tackle the trade-off between efficiency and effectiveness in PR, this paper proposes Graph Passage Retriever (GraphPR), a graph-based model inspired by the development of graph learning techniques. Different from existing works, GraphPR is end-to-end and integrates both term-matching information and semantics. GraphPR constructs a passage-level graph from BM25 retrieval results and trains a GCN-like model on the graph with graph-based objectives. Passages were regarded as nodes in the constructed graph and were embedded in dense vectors. PR can then be implemented using embeddings and a fast vector-similarity search. Experiments on a variety of real-world retrieval datasets show that the proposed model outperforms related models in several evaluation metrics (e.g., mean reciprocal rank, accuracy, F1-scores) while maintaining a relatively low query latency and memory usage.

Keywords: efficiency, effectiveness, graph learning, language model, passage retrieval, term-matching model

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Authors: Yahia. Kourd, N. Guersi D. Lefebvre

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In this paper we propose to study the faults diagnosis in squirrel-cage induction motor using MLP neural networks. We use neural healthy and faulty models of the behavior in order to detect and isolate some faults in machine. In the first part of this work, we have created a neural model for the healthy state using Matlab and a motor located in LGEB by acquirins data inputs and outputs of this engine. Then we detected the faults in the machine by residual generation. These residuals are not sufficient to isolate the existing faults. For this reason, we proposed additive neural networks to represent the faulty behaviors. From the analysis of these residuals and the choice of a threshold we propose a method capable of performing the detection and diagnosis of some faults in asynchronous machines with squirrel cage rotor.

Keywords: faults diagnosis, neural networks, multi-models, squirrel-cage induction motor

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Authors: Semih Sözer

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One of the most common issues in economic systems is understanding characteristics of economic activities in cities and regions. Although there are critics to economic base models in conceptual and empirical aspects, these models are useful tools to examining the economic structure of a nation, regions or cities. This paper uses one of the methodologies of economic base models namely the location quotients model. Data for this model includes employment numbers of provinces and NUTS II regions in Turkey. Time series of data covers the years of 1990, 2000, 2003, and 2009. Aim of this study is finding which sectors are export-base and which sectors are import-base in provinces and regions. Model results show that big provinces or powerful regions (population, size etc.) mostly have basic sectors in their economic system. However, interesting facts came from different sectors in different provinces and regions in the model results.

Keywords: economic base, location quotients model, regional economics, regional development

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Authors: Ridha Salhi, Mondher Labidi, Fethi Choubani

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Metamaterials have attracted much attention in recent years because of their electromagnetic exquisite proprieties. We will present, in this paper, the modeling of three metamaterial structures by equivalent circuit model. We begin by modeling the SRR (Split Ring Resonator), then we model the HIS (High Impedance Surfaces), and finally, we present the model of the CPW (Coplanar Wave Guide). In order to validate models, we compare the results obtained by an equivalent circuit models with numerical simulation.

Keywords: metamaterials, SRR, HIS, CPW, IDC

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Authors: Suraka Bhattacharjee, Ranjan Chaudhury

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The interaction between the charge degrees of freedom for itinerant antiferromagnets is investigated in terms of generalized charge stiffness constant corresponding to nearest neighbour t-J model and t1-t2-t3-J model. The low dimensional hole doped antiferromagnets are the well known systems that can be described by the t-J-like models. Accordingly, we have used these models to investigate the fermionic pairing possibilities and the coupling between the itinerant charge degrees of freedom. A detailed comparison between spin and charge couplings highlights that the charge and spin couplings show very similar behaviour in the over-doped region, whereas, they show completely different trends in the lower doping regimes. Moreover, a qualitative equivalence between generalized charge stiffness and effective Coulomb interaction is also established based on the comparisons with other theoretical and experimental results. Thus it is obvious that the enhanced possibility of fermionic pairing is inherent in the reduction of Coulomb repulsion with increase in doping concentration. However, the increased possibility can not give rise to pairing without the presence of any other pair producing mechanism outside the t-J model. Therefore, one can conclude that the t-J-like models themselves solely are not capable of producing conventional momentum-based superconducting pairing on their own.

Keywords: generalized charge stiffness constant, charge coupling, effective Coulomb interaction, t-J-like models, momentum-space pairing

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Authors: Tiah Rachmatiah, Subaryanti

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Plants of Lauraceae family are known to contain many chemical compounds which have potential bioactivity such as alkaloids, flavonoids, lactones, terpenes, etc. Actinodaphne macrophylla and Nectandra angustifolia are two species from Lauraceae. A previous study on the crude alkaloidal extract from the bark of Act. macrophylla and n-hexane extract from the bark of N. angustifolia showed antiplasmodial activity against Plasmodium falciparum. The study was continued to find antiplasmodial active compounds from the two extracts. The materials were obtained from Bogor Botanical Garden, West Java, Indonesia. Crude alkaloidal extract of Act. macrophylla was prepared by maceration in dichloromethane after moistened with NH4OH 25% and n-hexane extract of N. angustifolia was prepared by maceration in n-hexane. A major compound was isolated by column chromatography using silica gel and a mixture of CH2Cl2 and methanol as a gradient solvent system for the alkaloidal extract and mixture of n-hexane and ethyl acetate for n-hexane extract. Fine white needle crystals were obtained from the alkaloidal extract and rod crystals from n-hexane extract. Molecular structure of the compounds was determined by analysis of spectra of NMR, IR, MS and compared by references. In vitro bioactivity test of the compound was performed against Plasmodium falciparum. The results showed that the bark of Act. macrophylla contained an aporphine alkaloid, actinodaphnine, that had activity against P. falciparum with IC50 value of 0.095 µg/mL and the bark of N. angustifolia contained a lignan compound, sesamine, with IC50 of 0.122 µg/mL.

Keywords: actinodaphne macrophylla, alkaloid, antiplasmodial, lauraceae, lignan, nectandra angustifolia

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Authors: Luleka Dlamini, Olivier Crespo, Jos van Dam

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Accurately estimating crop growth and yield is pivotal for timely sustainable agricultural management and ensuring food security. Crop models and remote sensing can complement each other and form a robust analysis tool to improve crop growth and yield estimations when combined. This study thus aims to systematically evaluate how research that exclusively focuses on assimilating RS data into crop models varies among countries, crops, data assimilation methods, and farming conditions. A strict search string was applied in the Scopus and Web of Science databases, and 497 potential publications were obtained. After screening for relevance with predefined inclusion/exclusion criteria, 123 publications were considered in the final review. Results indicate that over 81% of the studies were conducted in countries associated with high socio-economic and technological advancement, mainly China, the United States of America, France, Germany, and Italy. Many of these studies integrated MODIS or Landsat data into WOFOST to improve crop growth and yield estimation of staple crops at the field and regional scales. Most studies use recalibration or updating methods alongside various algorithms to assimilate remotely sensed leaf area index into crop models. However, these methods cannot account for the uncertainties in remote sensing observations and the crop model itself. l. Over 85% of the studies were based on commercial and irrigated farming systems. Despite a great global interest in data assimilation into crop models, limited research has been conducted in resource- and data-limited regions like Africa. We foresee a great potential for such application in those conditions. Hence facilitating and expanding the use of such an approach, from which developing farming communities could benefit.

Keywords: crop models, remote sensing, data assimilation, crop yield estimation

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Authors: Luleka Dlamini, Olivier Crespo, Jos van Dam

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Accurately estimating crop growth and yield is pivotal for timely sustainable agricultural management and ensuring food security. Crop models and remote sensing can complement each other and form a robust analysis tool to improve crop growth and yield estimations when combined. This study thus aims to systematically evaluate how research that exclusively focuses on assimilating RS data into crop models varies among countries, crops, data assimilation methods, and farming conditions. A strict search string was applied in the Scopus and Web of Science databases, and 497 potential publications were obtained. After screening for relevance with predefined inclusion/exclusion criteria, 123 publications were considered in the final review. Results indicate that over 81% of the studies were conducted in countries associated with high socio-economic and technological advancement, mainly China, the United States of America, France, Germany, and Italy. Many of these studies integrated MODIS or Landsat data into WOFOST to improve crop growth and yield estimation of staple crops at the field and regional scales. Most studies use recalibration or updating methods alongside various algorithms to assimilate remotely sensed leaf area index into crop models. However, these methods cannot account for the uncertainties in remote sensing observations and the crop model itself. l. Over 85% of the studies were based on commercial and irrigated farming systems. Despite a great global interest in data assimilation into crop models, limited research has been conducted in resource- and data-limited regions like Africa. We foresee a great potential for such application in those conditions. Hence facilitating and expanding the use of such an approach, from which developing farming communities could benefit.

Keywords: crop models, remote sensing, data assimilation, crop yield estimation

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Authors: Rudzani Lusunzi, Frans Waanders, Elvis Fosso-Kankeu, Robert Khashane Netshitungulwana

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The Sabie Goldfield has a history of gold mining dating back more than a century. Acid mine drainage (AMD) from the Nestor mine tailings storage facility (MTSF) poses a serious threat to the nearby ecosystem, specifically the Sabie River system. This study aims at developing mitigation measures for the AMD emanating from the Nestor MTSF using materials from the Glynns Lydenburg MTSF. The Nestor MTSF (NM) and the Glynns Lydenburg MTSF (GM) each provided about 20 kg of bulk composite samples. Using samples from the Nestor MTSF and the Glynns Lydenburg MTSF, two mixtures were created. MIX-A is a mixture that contains 25% weight percent (GM) and 75% weight percent (NM). MIX-B is the name given to the second mixture, which contains 50% AN and 50% AG. The same static test, i.e., acid–base accounting (ABA), net acid generation (NAG), and acid buffering characteristics curve (ABCC) was used to estimate the acid-generating probabilities of samples NM and GM for MIX-A and MIX-B. Furthermore, the mineralogy of the Nestor MTSF samples consists of the primary acid-producing mineral pyrite as well as the secondary minerals ferricopiapite and jarosite, which are common in acidic conditions. The Glynns Lydenburg MTSF samples, on the other hand, contain primary acid-neutralizing minerals calcite and dolomite. Based on the assessment conducted, materials from the Glynns Lydenburg are capable of neutralizing AMD from Nestor MTSF. Therefore, the alkaline tailings materials from the Glynns Lydenburg MTSF can be used to rehabilitate the acidic Nestor MTSF.

Keywords: Nestor Mine, acid mine drainage, mitigation, Sabie River system

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Authors: Madad Ali

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In the contemporary landscape of international travel, the infusion of digital technologies has given rise to innovative business models that are reshaping the dynamics of tourism. This research delves into the transformative potential of these novel business models within the realm of digital tourism and their multifaceted impact on local businesses, residents' quality of life, and the overall travel experience. The study focuses on the captivating backdrop of Yunnan Province, China, renowned for its rich cultural heritage and diverse ethnic minorities, to uncover the intricate nuances of this phenomenon. The primary objectives of this research encompass the identification and categorization of emerging business models facilitated by digital technologies, their implications on tourist engagement, and their integration into the operations of local businesses. By employing a mixed-methods approach, blending qualitative techniques like interviews and content analysis with quantitative tools such as surveys and data analysis, the study provides a comprehensive evaluation of these business models' effects on various dimensions of the tourism landscape. The distinctiveness of this research lies in its exclusive focus on Yunnan Province, China. By concentrating on Yunnan Province, the research contributes exceptional insights into the interplay between digital tourism, ethnic diversity, cultural heritage, and sustainable development. The study's outcomes hold significance for both scholarly discourse and the stakeholders involved in shaping the region's tourism strategies.

Keywords: business model, digital tourism, international travel, local businesses, quality of life

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Authors: T. M. Agbede, A. O. Adekiya

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Field experiments were conducted during 2013, 2014 and 2015 cropping seasons at Rufus Giwa Polytechnic, Owo, Ondo State, southwest Nigeria. The objective of the investigation was to determine the effect of Cocoa Pod Ash (CPA) and Poultry Manure (PM) applied solely and their combined form, as sources of fertilizers on soil properties, leaf nutrient composition, growth and yield of yam. Three soil amendments: CPA, PM (sole forms), CPA and PM (mixture), were applied at 20 t ha^-1 with an inorganic fertilizer (NPK 15-15-15) at 400 kg ha^-1 as a reference and a natural soil fertility, NSF (control). The five treatments were arranged in a randomized complete block design with three replications. The test soil was slightly acidic, low in organic carbon (OC), N, P, K, Ca and Mg. Results showed that soil amendments significantly increased (p = 0.05) tuber weights and growth of yam, soil and leaf N, P, K, Ca and Mg, soil pH and OC concentrations compared with the NSF (control). The mixture of CPA+PM treatment increased tuber weights of yam by 36%, compared with inorganic fertilizer (NPK) and 19%, compared with PM alone. Sole PM increased tuber weight of yam by 15%, compared with NPK. Sole or mixed forms of soil amendments showed remarkable improvement in soil physical properties, nutrient availability, compared with NPK and the NSF (control). Integrated application of CPA at 10 t ha^-1 + PM at 10 t ha^-1 was the most effective treatment in improving soil physical properties, increasing nutrient availability and yam performance than sole application of any of the fertilizer materials.

Keywords: cocoa pod ash, leaf nutrient composition, poultry manure, soil properties, yam

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Authors: Javad Saeidaskari, Mohammad Hassan Baziar

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Soil structures have been frequently damaged during piping, earthquake and other types of failures. As far as adverse circumstances were developed subsequent to piping or other similar failure types, hydraulic parameters of soil such as hydraulic conductivity should be considered. As a result, acquiring an approach to diminish soil permeability is inevitable. There are many ground improvement methods to reduce seepage, which are classified under soil treatment and stabilization methods. Recently, one of the soil improvement methods is known as nanogeotechnology. This study aims to investigate the influence of Cloisite 30B nanoclay on permeability of compacted clayey sand soils. The samples are prepared by mixing two soil types, including Kaolin clay and Firouzkooh sand, in 1:9 and 1:5 clay:sand (by mass) proportions. In experimental procedure, initially, the optimum water content and maximum dry unit weight of each samples were obtained for compaction. Then, series of permeability tests were conducted by triaxial apparatus on prepared specimens with identical relative density of 95% of maximum dry density and water content of 1% wet of optimum for different weight percentages of nanoclay (1% to 4%). Therefore, in this paper, the effect of time on treated specimen was appraised, as well as two approaches of manual mixing and ball milling were compared to reveal the importance of dispersion issue. The results show that adding nanoclay up to 3%, as its optimum content, causes notable reduction in permeability (1.60e-03 to 5.51e-05 cm/s and 3.32e-04 to 8.44e-07 cm/s in samples with 1:9 and 1:5 mixture proportions, respectively). The hydraulic conductivity of treated clayey sand (1:5 mixture proportion with 3% nanoclay) decreases gradually from 8.44e-07 to 3.00e-07 cm/s within 90 days and then tends to be consistent. The influence of mixing method on permeability results shows that the utilization of ball mill mixing effectively leads to lower values than those of manual mixing, in other words, by adding 3% nanoclay, hydraulic conductivity of specimen declines from 8.44e-07 to 2.00e-07 cm/s. In order to evaluate the interaction between soil particles and, to ensure proper dispersion of nanoparticles through clayey sand mixture, they were magnified by means of scanning electron microscope (SEM). In conclusion, the nanoclay particles in vicinity of moisture can cause soil stabilization to prevent water penetration, which eventually result in lower usage of clay and operation costs.

Keywords: nanoclay, cloisite 30b, clayey sand, hydraulic conductivity

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Authors: Arindam Chaudhuri

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Classification of data has been actively used for most effective and efficient means of conveying knowledge and information to users. The prima face has always been upon techniques for extracting useful knowledge from data such that returns are maximized. With emergence of huge datasets the existing classification techniques often fail to produce desirable results. The challenge lies in analyzing and understanding characteristics of massive data sets by retrieving useful geometric and statistical patterns. We propose a supervised parallel fuzzy rough support vector machine (PFRSVM) for data classification in cloud environment. The classification is performed by PFRSVM using hyperbolic tangent kernel. The fuzzy rough set model takes care of sensitiveness of noisy samples and handles impreciseness in training samples bringing robustness to results. The membership function is function of center and radius of each class in feature space and is represented with kernel. It plays an important role towards sampling the decision surface. The success of PFRSVM is governed by choosing appropriate parameter values. The training samples are either linear or nonlinear separable. The different input points make unique contributions to decision surface. The algorithm is parallelized with a view to reduce training times. The system is built on support vector machine library using Hadoop implementation of MapReduce. The algorithm is tested on large data sets to check its feasibility and convergence. The performance of classifier is also assessed in terms of number of support vectors. The challenges encountered towards implementing big data classification in machine learning frameworks are also discussed. The experiments are done on the cloud environment available at University of Technology and Management, India. The results are illustrated for Gaussian RBF and Bayesian kernels. The effect of variability in prediction and generalization of PFRSVM is examined with respect to values of parameter C. It effectively resolves outliers’ effects, imbalance and overlapping class problems, normalizes to unseen data and relaxes dependency between features and labels. The average classification accuracy for PFRSVM is better than other classifiers for both Gaussian RBF and Bayesian kernels. The experimental results on both synthetic and real data sets clearly demonstrate the superiority of the proposed technique.

Keywords: FRSVM, Hadoop, MapReduce, PFRSVM

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Authors: Melodi Mbuyi Mata, Blessing Olamide Taiwo, Afolabi Ayodele David

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The purpose of this research is to create empirical models for assessing the profitability of granite aggregate production in Akure, Ondo state aggregate quarries. In addition, an artificial neural network (ANN) model and multivariate predicting models for granite profitability were developed in the study. A formal survey questionnaire was used to collect data for the study. The data extracted from the case study mine for this study includes granite marketing operations, royalty, production costs, and mine production information. The following methods were used to achieve the goal of this study: descriptive statistics, MATLAB 2017, and SPSS16.0 software in analyzing and modeling the data collected from granite traders in the study areas. The ANN and Multi Variant Regression models' prediction accuracy was compared using a coefficient of determination (R²), Root mean square error (RMSE), and mean square error (MSE). Due to the high prediction error, the model evaluation indices revealed that the ANN model was suitable for predicting generated profit in a typical quarry. More quarries in Nigeria's southwest region and other geopolitical zones should be considered to improve ANN prediction accuracy.

Keywords: national development, granite, profitability assessment, ANN models

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Authors: Gianella Franco, Karen Suarez, María Quijano, Patricia Manzano

Abstract:

Agro-industries generate large amounts of waste, which are mostly untapped. This research was carried out to use cocoa, coffee and orange industrial by-products to develop a dairy drink. The product was prepared by making a 10% aqueous extract of the mixture of cocoa and coffee beans shells and orange peel. Extreme Vertices Mixture Design was applied to vary the proportions of the ingredients of the aqueous extract, getting 13 formulations. Each formulation was mixed with skim milk and pasteurized. The attributes of taste, smell, color and appearance were evaluated by a semi-trained panel by multiple comparisons test, comparing the formulations against a standard marked as "R", which consisted of a coffee commercial drink. The formulations with the highest scores were selected to maximize the Total Polyphenol Content (TPC) through a process of linear optimization resulting in the formulation 80.5%: 18.37%: 1.13% of cocoa bean shell, coffee bean shell and orange peel, respectively. The Total Polyphenol Content was 4.99 ± 0.34 mg GAE/g of drink, DPPH radical scavenging activity (%) was 80.14 ± 0.05 and caffeine concentration of 114.78 mg / L, while the coffee commercial drink presented 3.93 ± 0.84 mg GAE / g drink, 55.54 ± 0.03 % and 47.44 mg / L of TPC, DPPH radical scavenging activity and caffeine content, respectively. The results show that it is possible to prepare an antioxidant - rich drink with good sensorial attributes made of industrial by-products.

Keywords: DPPH, polyphenols, waste, food science

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Authors: Mohd Raish Ansari, Tauheed Alam Khan

Abstract:

This research paper is a comparative study of the belt truss in the Regular RC frame structure at the different positions of the floor. The method used in this research is the response spectrum method with the help of the ETABS Software, there are six models in this paper with belt truss. The Indian standard code used in this work are IS 456:2000, IS 800:2007, IS 875 part-1, IS 875 part-1, and IS 1893 Part-1:2016. The cross-section of the belt truss is the I-section, a grade of steel that is made up of Mild Steel. The basic model in this research paper is the same, only position of the belt truss is going to change, and the dimension of the belt truss is remain constant for all models. The plan area of all models is 24.5 meters x 28 meters, and the model has G+20, where the height of the ground floor is 3.5 meters, and all floor height is 3.0 meters remains constant. This comparative research work selected some important seismic parameters to check the stability of all models, the parameters are base shear, fundamental period, storey overturning moment, and maximum storey displacement.

Keywords: belt truss, RC frames structure, ETABS, response spectrum analysis, special moment resisting frame

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Authors: Ishapathik Das

Abstract:

The purpose of this article is to find a method of comparing designs for ordinal regression models using quantile dispersion graphs in the presence of linear predictor misspecification. The true relationship between response variable and the corresponding control variables are usually unknown. Experimenter assumes certain form of the linear predictor of the ordinal regression models. The assumed form of the linear predictor may not be correct always. Thus, the maximum likelihood estimates (MLE) of the unknown parameters of the model may be biased due to misspecification of the linear predictor. In this article, the uncertainty in the linear predictor is represented by an unknown function. An algorithm is provided to estimate the unknown function at the design points where observations are available. The unknown function is estimated at all points in the design region using multivariate parametric kriging. The comparison of the designs are based on a scalar valued function of the mean squared error of prediction (MSEP) matrix, which incorporates both variance and bias of the prediction caused by the misspecification in the linear predictor. The designs are compared using quantile dispersion graphs approach. The graphs also visually depict the robustness of the designs on the changes in the parameter values. Numerical examples are presented to illustrate the proposed methodology.

Keywords: model misspecification, multivariate kriging, multivariate logistic link, ordinal response models, quantile dispersion graphs

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Authors: Hemin Yuan, Yun Wang, Xiangchun Wang

Abstract:

Gas hydrate is drawing attention due to the fact that it has an enormous amount all over the world, which is almost twice the conventional hydrocarbon reserves, making it a potential alternative source of energy. It is widely distributed in permafrost and continental ocean shelves, and many countries have launched national programs for investigating the gas hydrate. Gas hydrate is mainly explored through seismic methods, which include bottom simulating reflectors (BSR), amplitude blanking, and polarity reverse. These seismic methods are effective at finding the gas hydrate formations but usually contain large uncertainties when applying to invert the micro-scale petrophysical properties of the formations due to lack of constraints. Rock physics modeling links the micro-scale structures of the rocks to the macro-scale elastic properties and can work as effective constraints for the seismic methods. A number of rock physics models have been proposed for gas hydrate modeling, which addresses different mechanisms and applications. However, these models are generally not well classified, and it is confusing to determine the appropriate model for a specific study. Moreover, since the modeling usually involves multiple models and steps, it is difficult to determine the source of uncertainties. To solve these problems, we summarize the developed models/methods and make four classifications of the models according to the hydrate micro-scale morphology in sediments, the purpose of reservoir characterization, the stage of gas hydrate generation, and the lithology type of hosting sediments. Some sub-categories may overlap each other, but they have different priorities. Besides, we also analyze the priorities of different models, bring up the shortcomings, and explain the appropriate application scenarios. Moreover, by comparing the models, we summarize a general workflow of the modeling procedure, which includes rock matrix forming, dry rock frame generating, pore fluids mixing, and final fluid substitution in the rock frame. These procedures have been widely used in various gas hydrate modeling and have been confirmed to be effective. We also analyze the potential sources of uncertainties in each modeling step, which enables us to clearly recognize the potential uncertainties in the modeling. In the end, we explicate the general problems of the current models, including the influences of pressure and temperature, pore geometry, hydrate morphology, and rock structure change during gas hydrate dissociation and re-generation. We also point out that attenuation is also severely affected by gas hydrate in sediments and may work as an indicator to map gas hydrate concentration. Our work classifies rock physics models of gas hydrate into different categories, generalizes the modeling workflow, analyzes the modeling uncertainties and potential problems, which can facilitate the rock physics characterization of gas hydrate bearding sediments and provide hints for future studies.

Keywords: gas hydrate, rock physics model, modeling classification, hydrate morphology

Procedia PDF Downloads 138