Search results for: quantifying the water volume from nozzle

Authors: Khaled Al-Badani, James Ren, Lisa Li, David Allanson

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Droplet formation is an important process in many engineering systems and manufacturing procedures, which includes welding, biotechnologies, 3D printing, biochemical, biomedical fields and many more. The volume and the characteristics of droplet formation are generally depended on various material properties, microfluidics and fluid mechanics considerations. Hence, a detailed investigation of this process, with the aid of numerical computational tools, are essential for future design optimization and process controls of many engineering systems. This will also improve the understanding of changes in the properties and the structures of materials, during the formation of the droplet, which is important for new material developments to achieve different functions, pending the requirements of the application. For example, the shape of the formed droplet is critical for the function of some final products, such as the welding nugget during Capacitor Discharge Welding process, or PLA 3D printing, etc. Although, most academic journals on droplet formation, focused on issued with material transfer rate, surface tension and residual stresses, the general emphasis on the characteristics of droplet shape has been overlooked. The proposed work for this project will examine theoretical methodologies, experimental techniques, and numerical modelling, using ANSYS FLUENT, to critically analyse and highlight optimization methods regarding the formation of pendant droplet. The project will also compare results from published data with experimental and numerical work, concerning the effects of key material parameters on the droplet shape. These effects include changes in heating/cooling rates, solidification/melting progression and separation/break-up times. From these tests, a set of objectives is prepared, with an intention of improving quality, stability and productivity in modelling metal welding and 3D printing.

Keywords: computer modelling, droplet formation, material distortion, materials forming, welding

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Authors: Vigen Barkhudaryan

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The influence of external effects (γ-, UV–radiations, high temperature) in presence of air oxygen on structural transformations of low-density polyethylene (LDPE) have been investigated dependent on the polymers’ thickness, the intensity and the dose of external actions. The methods of viscosimetry, light scattering, turbidimetry and gelation measuring were used for this purpose. The comparison of influence of external effects on LDPE shows, that the destruction and cross-linking processes of macromolecules proceed simultaneously with all kinds of external effects. A remarkable growth of average molecular mass of LDPE along with the irradiation doses and heat treatment exposure growth was established. It was linear for the mass average molecular mass and at the initial doses is mainly the result of the increase of the macromolecular branching. As a result, the macromolecular hydrodynamic volumes have been changed, and therefore the dependence of viscosity average molecular mass on the doses was going through the minimum at initial doses. A significant change of molecular mass, sizes and shape of macromolecules of LDPE occurs under the influence of external effects. The influence is limited only by diffusion of oxygen during -irradiation and heat treatment. At UV–irradiation the influence is limited both by diffusion of oxygen and penetration of radiation. Consequently, the molecular transformations are deeper and evident in case of -irradiation, as soon as the polymer is transformed in a whole volume. It was also established, that the mechanism of molecular transformations in polymers from the surface layer distinctly differs from those of the sample deeper layer. A comparison of the results of these investigations allows us to conclude, that the mechanisms of influence of investigated external effects on polyethylene are similar.

Keywords: cross-linking, destruction, high temperature, LDPE, γ-radiations, UV-radiations

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Authors: Donald Wright

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The ultimate goal of this study is to reinvigorate the philosophical underpinnings the study of urbanization with scientific data with the goal of circumventing what seems an inevitable future clash between rural and urban populations. To that end urban infrastructure must be sustainable economically, politically and ecologically over the course of several generations as cities continue to grow with the incorporation of climate refugees. Our research will provide data concerning the projected increase in population over the coming two decades in Morocco, and the population will shift from rural areas to urban centers during that period of time. As a result, urban infrastructure will need to be adapted, developed or built to fit the demand of future internal migrations from rural to urban centers in Morocco. This paper will also examine how past experiences of internally displaced people give insight into the challenges faced by future migrants and, beyond the gathering of data, how people react to internal migration. This study employs four different sets of research tools. First, a large part of this study is archival, which involves compiling the relevant literature on the topic and its complex history. This step also includes gathering data bout migrations in Morocco from public data sources. Once the datasets are collected, the next part of the project involves populating the attribute fields and preprocessing the data to make it understandable and usable by machine learning algorithms. In tandem with the mathematical interpretation of data and projected migrations, this study benefits from a theoretical understanding of the critical apparatus existing around urban development of the 20th and 21st centuries that give us insight into past infrastructure development and the rationale behind it. Once the data is ready to be analyzed, different machine learning algorithms will be experimented (k-clustering, support vector regression, random forest analysis) and the results compared for visualization of the data. The final computational part of this study involves analyzing the data and determining what we can learn from it. This paper helps us to understand future trends of population movements within and between regions of North Africa, which will have an impact on various sectors such as urban development, food distribution and water purification, not to mention the creation of public policy in the countries of this region. One of the strengths of this project is the multi-pronged and cross-disciplinary methodology to the research question, which enables an interchange of knowledge and experiences to facilitate innovative solutions to this complex problem. Multiple and diverse intersecting viewpoints allow an exchange of methodological models that provide fresh and informed interpretations of otherwise objective data.

Keywords: climate change, machine learning, migration, Morocco, urban development

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Authors: Sajedeh Salehi, Marco Giacalone

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This paper examines the role of existing online dispute resolution (ODR) mechanisms and their effects on ameliorating access to justice – as a protected right by Art. 47 of the EU Charter of Fundamental Rights – for consumers in EU. The major focus of this study will be on evaluating ODR as the means of dispute resolution for Business-to-Consumer (B2C) cross-border small claims raised in e-commerce transactions. The authors will elaborate the consequences of implementing ODR methods in the context of recent developments in EU regulatory safeguards on promoting consumer protection. In this analysis, both non-judiciary and judiciary ODR redress mechanisms are considered, however, the significant consideration is given to – obligatory and non-obligatory – judiciary ODR methods. For that purpose, this paper will particularly investigate the impact of the EU ODR platform as well as the European Small Claims Procedure (ESCP) Regulation 861/2007 and their role on accelerating the access to justice for consumers in B2C e-disputes. Although, considerable volume of research has been carried out on ODR for consumer claims, rather less (or no-) attention has been paid to provide a combined doctrinal and empirical evaluation of ODR’s potential in resolving cross-border small e-disputes, in EU. Hence, the methodological approach taken in this study is a mixed methodology based on qualitative (interviews) and quantitative (surveys) research methods which will be mainly based on the data acquired through the findings of the Small Claims Analysis Net (SCAN) project. This project contributes towards examining the ESCP Regulation implementation and efficiency in providing consumers with a legal watershed through using the ODR for their transnational small claims. The outcomes of this research may benefit both academia and policymakers at national and international level.

Keywords: access to justice, consumers, e-commerce, small e-Disputes

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Authors: Shihab Aldin Al Riyami, Khosrow Ebrahim, Sajad Ahmadizad

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Hemodynamic changes and ventricular loading during exercise lead to left ventricular (LV) hypertrophy. In athletes, volume load induces enlargement of the LV internal diameter and a proportional increase of wall thickness; while, pressure load would induce thickening of the ventricular wall. These adaptations are not similar in all athletes and are related to the types of sport. Volleyball players have different types of activity and roles based on their playing. Therefore, their physiological adaptations and requirements are different. The aim of the current study was to investigate the LV adaptationsinelite volleyball players based on their playing position. Sixty male elite volleyball players (age, 30.55±3.64 years)from Brazil, Serbia, Poland, Iran, Colombia, Cameroon, Japan, Egypt, Qatar, and Tunisia were investigated (from all five volleyball play positions). All participants had the experience of at least 3 years of participation at a professional level and international tournaments. LV characteristics were evaluated and measured using the echocardiography technique. Statistical analyses revealed significant differences (P<0.05)among the five groups of players forLV internal dimension (LVID), posterior wall thickness (PWT), and intact ventricular septum (IVS). Post-hoc analysis showed that opposite position players had significant higher value of LVID, PWT, and IVS when compared with other players, including outside hitter, middle blocker, setter, and libero (p<0.05). Additionally, in libero players, PWT was significantly lower when compared with other players (p<0.05). Based on the findings of the present study, it is concluded that LV adaptations in volleyball players are related to their playing position and that the opposite players had the highest LV adaptations when compared to other positions.

Keywords: athletes, cardiac adaptations, echocardio graphy, heart, sport

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Authors: Joao Victor Borges Dos Santos, Thomas Richard, Yevhen Kovalyshen

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Down-the-hole (DTH) percussive drilling is an excavation method that is widely used in the mining industry due to its high efficiency in fragmenting hard rock formations. A DTH hammer system consists of a fluid driven (air or water) piston and a drill bit; the reciprocating movement of the piston transmits its kinetic energy to the drill bit by means of stress waves that propagate through the drill bit towards the rock formation. In the literature of percussive drilling, the existence of an optimum drilling state (Sweet Spot) is reported in some laboratory and field experimental studies. An optimum rate of penetration is achieved for a specific range of axial thrust (or weight-on-bit) beyond which the rate of penetration decreases. Several authors advance different explanations as possible root causes to the occurrence of the Sweet Spot, but a universal explanation or consensus does not exist yet. The experimental investigation in this work was initiated with drilling experiments conducted at a mining site. A full-scale drilling rig (equipped with a DTH hammer system) was instrumented with high precision sensors sampled at a very high sampling rate (kHz). Data was collected while two boreholes were being excavated, an in depth analysis of the recorded data confirmed that an optimum performance can be achieved for specific ranges of input thrust (weight-on-bit). The high sampling rate allowed to identify the bit penetration at each single impact (of the piston on the drill bit) as well as the impact frequency. These measurements provide a direct method to identify when the hammer does not fire, and drilling occurs without percussion, and the bit propagate the borehole by shearing the rock. The second stage of the experimental investigation was conducted in a laboratory environment with a custom-built equipment dubbed Woody. Woody allows the drilling of shallow holes few centimetres deep by successive discrete impacts from a piston. After each individual impact, the bit angular position is incremented by a fixed amount, the piston is moved back to its initial position at the top of the barrel, and the air pressure and thrust are set back to their pre-set values. The goal is to explore whether the observed optimum drilling state stems from the interaction between the drill bit and the rock (during impact) or governed by the overall system dynamics (between impacts). The experiments were conducted on samples of Calca Red, with a drill bit of 74 millimetres (outside diameter) and with weight-on-bit ranging from 0.3 kN to 3.7 kN. Results show that under the same piston impact energy and constant angular displacement of 15 degrees between impact, the average drill bit rate of penetration is independent of the weight-on-bit, which suggests that the sweet spot is not caused by intrinsic properties of the bit-rock interface.

Keywords: optimum drilling state, experimental investigation, field experiments, laboratory experiments, down-the-hole percussive drilling

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Authors: Krishmali N. Ekanayake, Brendan J. Holland, Colin J. Barrow, Rick Wood

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Globally, more than one-third of all food produced is lost or wasted, equating to 1.3 billion tonnes per year. Around 31.2 million tonnes of food waste are generated across the production, supply, and consumption chain in Australia. Generally, the food waste management processes adopt environmental-friendly and more sustainable approaches such as composting, anerobic digestion and energy implemented technologies. However, unavoidable, and non-recyclable food waste ends up as landfilling and incineration that involve many undesirable impacts and challenges on the environment. A biorefinery approach contributes to a waste-minimising circular economy by converting food and other organic biomass waste into valuable outputs, including feeds, nutrition, fertilisers, and biomaterials. As a solution, Green Eco Technologies has developed a food waste treatment process using WasteMaster system. The system uses charged oxygen and moderate temperatures to convert food waste, without bacteria, additives, or water, into a virtually odour-free, much reduced quantity of reusable residual material. In the context of a biorefinery, the WasteMaster dries and mills food waste into a form suitable for storage or downstream extraction/separation/concentration to create products. The focus of the study is to determine the nutritional composition of WasteMaster processed residue to potential develop aquafeed ingredients. The global aquafeed industry is projected to reach a high value market in future, which has shown high demand for the aquafeed products. Therefore, food waste can be utilized for aquaculture feed development by reducing landfill. This framework will lessen the requirement of raw crops cultivation for aquafeed development and reduce the aquaculture footprint. In the present study, the nutritional elements of processed residue are consistent with the input food waste type, which has shown that the WasteMaster is not affecting the expected nutritional distribution. The macronutrient retention values of protein, lipid, and nitrogen free extract (NFE) are detected >85%, >80%, and >95% respectively. The sensitive food components including omega 3 and omega 6 fatty acids, amino acids, and phenolic compounds have been found intact in each residue material. Preliminary analysis suggests a price comparability with current aquafeed ingredient cost making the economic feasibility. The results suggest high potentiality of aquafeed development as 5 to 10% of the ingredients to replace/partially substitute other less sustainable ingredients across biorefinery setting. Our aim is to improve the sustainability of aquaculture and reduce the environmental impacts of food waste.

Keywords: biorefinery, ffood waste residue, input, wasteMaster

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Authors: Rabeb Triki, Hassene Djemel, Mounir Baccar

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Surface scraping is a passive heat transfer enhancement technique that is directly used in scraped surface heat exchanger (SSHE). The scraping action prevents the accumulation of the product on the inner wall, which intensifies the heat transfer and avoids the formation of dead zones. SSHEs are widely used in industry for several applications such as crystallization, sterilization, freezing, gelatinization, and many other continuous processes. They are designed to deal with products that are viscous, sticky or that contain particulate matter. This research work presents a three-dimensional numerical simulation of the coupled thermal and hydrodynamic behavior within a SSHE which includes Archimedes’ screw instead of scraper blades. The finite volume Fluent 15.0 was used to solve continuity, momentum and energy equations using multiple reference frame formulation. The process fluid investigated under this study is the pure glycerin. Different geometrical parameters were studied in the case of steady, non-isothermal, laminar flow. In particular, attention is focused on the effect of the conicity of the rotor and the pitch of Archimedes’ screw on temperature and velocity distribution and heat transfer rate. Numerical investigations show that the increase of the number of turns in the screw from five to seven turns leads to amelioration of heat transfer coefficient, and the increase of the conicity of the rotor from 0.1 to 0.15 leads to an increase in the rate of heat transfer. Further studies should investigate the effect of different operating parameters (axial and rotational Reynolds number) on the hydrodynamic and thermal behavior of the SSHE.

Keywords: ANSYS-Fluent, hydrodynamic behavior, scraped surface heat exchange, thermal behavior

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Authors: Indu Chauhan, Bhupendra S. Butola, Paritosh Mohanty

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It is very well known that properties of material changes as their size approach to nanoscale level due to the high surface area to volume ratio. However, in last few decades, a tenet ‘structure dictates function’ is quickly being adopted by researchers working with nanomaterials. The design and exploitation of nanoparticles with tailored shape and size has become one of the primary goals of materials science researchers to expose the properties of nanostructures. To date, various methods, including soft/hard template/surfactant assisted route hydrothermal reaction, seed mediated growth method, capping molecule-assisted synthesis, polyol process, etc. have been adopted to synthesize the nanostructures with controlled size and shape and monodispersity. However controlling the shape and size of nanoparticles is an ultimate challenge of modern material research. In particular, many efforts have been devoted to rational and skillful control of hierarchical and complex nanostructures. Thus in our research work, role of cellulose in manipulating the nanostructures has been discussed. Nanoparticles of α-Fe₂O₃ (diameter ca. 15 to 130 nm) were immobilized on the cellulose fiber surface by a single step in situ hydrothermal method. However, nanoflakes of α-FeOOH having thickness ca. ~25 nm and length ca. ~250 nm were obtained by the same method in absence of cellulose fibers. A possible nucleation and growth mechanism of the formation of nanostructures on cellulose fibers have been proposed. The covalent bond formation between the cellulose fibers and nanostructures has been discussed with supporting evidence from the spectroscopic and other analytical studies such as Fourier transform infrared spectroscopy and X-ray photoelectron spectroscopy. The role of cellulose in manipulating the nanostructures has been discussed.

Keywords: cellulose fibers, α-Fe₂O₃, α-FeOOH, hydrothermal, nanoflakes, nanoparticles

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Authors: Sidi Ahmed Berrabah

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English as a Medium of Instruction (EMI) is expanding rapidly in the world. A growing volume of research has been dedicated to investigating its introduction, with findings that describe a complex picture and suggest that the practicality and effectiveness of EMI are still the subjects of debate. However, considerably less attention has been given to understanding EMI in a context where its introduction has been discussed but not yet put into practice. One such context is Algeria, where discourses about a potential introduction of EMI have been going on for some time. It is likely that the first courses where EMI is introduced are Business degree programmes. This study aims to examine the current discourses and attitudes towards the potential implementation of EMI and the language practices in Business degree programmes in three Algerian universities. The research is conducted in three different universities in three different regions in Algeria with the aim of including both ‘centre’ and ‘periphery’ Algerian universities. In order to achieve the previous aims, a mixed research paradigm is used. Questionnaires, semi structured interviews, and classroom observations are used to gather data from three participant cohorts: university students of Business, lecturers of Business, and lecturers of English for specific purposes. The findings showed that students and lecturers of Business are found in favour of the introduction of English instead of French or standard Arabic as a medium of instruction. The reason is that English is seen as having internationalisation and instrumental benefits, while French was too closely linked to the colonial history of the country. The favourable attitudes towards EMI, however, seem to contrast with the daily classroom practices at the departments of Business studies, where students and lecturers make practical choices of using their language repertoire based on their linguistic background and skills. Classrooms in the three Algerian universities featured fluid and translanguaging practices that cannot be reduced to a monolingual EMI policy.

Keywords: EMI, Algerian universities, business degree programmes, translanguaging

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Authors: Ananya Ananya, Karthik Rao

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Accurate segmentation of knee cartilage in 3-D magnetic resonance (MR) images for quantitative assessment of volume is crucial for studying and diagnosing osteoarthritis (OA) of the knee, one of the major causes of disability in elderly people. Radiologists generally perform this task in slice-by-slice manner taking 15-20 minutes per 3D image, and lead to high inter and intra observer variability. Hence automatic methods for knee cartilage segmentation are desirable and are an active field of research. This paper presents design and experimental evaluation of 2D convolutional neural networks based fully automated methods for knee cartilage segmentation in 3D MRI. The architectures are validated based on 40 test images and 60 training images from SKI10 dataset. The proposed methods segment 2D slices one by one, which are then combined to give segmentation for whole 3D images. Proposed methods are modified versions of U-net and dilated convolutions, consisting of a single step that segments the given image to 5 labels: background, femoral cartilage, tibia cartilage, femoral bone and tibia bone; cartilages being the primary components of interest. U-net consists of a contracting path and an expanding path, to capture context and localization respectively. Dilated convolutions lead to an exponential expansion of receptive field with only a linear increase in a number of parameters. A combination of modified U-net and dilated convolutions has also been explored. These architectures segment one 3D image in 8 – 10 seconds giving average volumetric Dice Score Coefficients (DSC) of 0.950 - 0.962 for femoral cartilage and 0.951 - 0.966 for tibia cartilage, reference being the manual segmentation.

Keywords: convolutional neural networks, dilated convolutions, 3 dimensional, fully automated, knee cartilage, MRI, segmentation, U-net

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Authors: Hamdi Beji, Toufik Kanit, Tanguy Messager

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This study aims to construct a predictive model proficient in foreseeing the linear elastic and thermal characteristics of composite materials, drawing on a multitude of influencing parameters. These parameters encompass the shape of inclusions (circular, elliptical, square, triangle), their spatial coordinates within the matrix, orientation, volume fraction (ranging from 0.05 to 0.4), and variations in contrast (spanning from 10 to 200). A variety of machine learning techniques are deployed, including decision trees, random forests, support vector machines, k-nearest neighbors, and an artificial neural network (ANN), to facilitate this predictive model. Moreover, this research goes beyond the predictive aspect by delving into an inverse analysis using genetic algorithms. The intent is to unveil the intrinsic characteristics of composite materials by evaluating their thermomechanical responses. The foundation of this research lies in the establishment of a comprehensive database that accounts for the array of input parameters mentioned earlier. This database, enriched with this diversity of input variables, serves as a bedrock for the creation of machine learning and genetic algorithm-based models. These models are meticulously trained to not only predict but also elucidate the mechanical and thermal conduct of composite materials. Remarkably, the coupling of machine learning and genetic algorithms has proven highly effective, yielding predictions with remarkable accuracy, boasting scores ranging between 0.97 and 0.99. This achievement marks a significant breakthrough, demonstrating the potential of this innovative approach in the field of materials engineering.

Keywords: machine learning, composite materials, genetic algorithms, mechanical and thermal proprieties

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Authors: Behrang Tavousi Tehrani, Mohammad-Zaman Kabir

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Shape memory alloys (SMA) are often implemented in smart structures as the active components. Their ability to recover large displacements has been used in many applications, including structural stability/response enhancement and active structural acoustic control. SMA wires or fibers can be embedded with composite cylinders to increase their critical buckling load, improve their load-deflection behavior, and reduce the radial deflections under various thermo-mechanical loadings. This paper presents a semi-analytical investigation on the non-linear load-deflection response of SMA-reinforced composite circular cylindrical shells. The cylinder shells are under uniform external pressure load. Based on first-order shear deformation shell theory (FSDT), the equilibrium equations of the structure are derived. One-dimensional simplified Brinson’s model is used for determining the SMA recovery force due to its simplicity and accuracy. Airy stress function and Galerkin technique are used to obtain non-linear load-deflection curves. The results are verified by comparing them with those in the literature. Several parametric studies are conducted in order to investigate the effect of SMA volume fraction, SMA pre-strain value, and SMA activation temperature on the response of the structure. It is shown that suitable usage of SMA wires results in a considerable enhancement in the load-deflection response of the shell due to the generation of the SMA tensile recovery force.

Keywords: airy stress function, cylindrical shell, Galerkin technique, load-deflection curve, recovery stress, shape memory alloy

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Authors: Amin Salighehdar, Ziwen Ye, Mingzhe Liu, Ionut Florescu, Alan F. Blumberg

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Storm surge is an abnormal water level caused by a storm. Accurate prediction of a storm surge is a challenging problem. Researchers developed various ensemble modeling techniques to combine several individual forecasts to produce an overall presumably better forecast. There exist some simple ensemble modeling techniques in literature. For instance, Model Output Statistics (MOS), and running mean-bias removal are widely used techniques in storm surge prediction domain. However, these methods have some drawbacks. For instance, MOS is based on multiple linear regression and it needs a long period of training data. To overcome the shortcomings of these simple methods, researchers propose some advanced methods. For instance, ENSURF (Ensemble SURge Forecast) is a multi-model application for sea level forecast. This application creates a better forecast of sea level using a combination of several instances of the Bayesian Model Averaging (BMA). An ensemble dressing method is based on identifying best member forecast and using it for prediction. Our contribution in this paper can be summarized as follows. First, we investigate whether the ensemble models perform better than any single forecast. Therefore, we need to identify the single best forecast. We present a methodology based on a simple Bayesian selection method to select the best single forecast. Second, we present several new and simple ways to construct ensemble models. We use correlation and standard deviation as weights in combining different forecast models. Third, we use these ensembles and compare with several existing models in literature to forecast storm surge level. We then investigate whether developing a complex ensemble model is indeed needed. To achieve this goal, we use a simple average (one of the simplest and widely used ensemble model) as benchmark. Predicting the peak level of Surge during a storm as well as the precise time at which this peak level takes place is crucial, thus we develop a statistical platform to compare the performance of various ensemble methods. This statistical analysis is based on root mean square error of the ensemble forecast during the testing period and on the magnitude and timing of the forecasted peak surge compared to the actual time and peak. In this work, we analyze four hurricanes: hurricanes Irene and Lee in 2011, hurricane Sandy in 2012, and hurricane Joaquin in 2015. Since hurricane Irene developed at the end of August 2011 and hurricane Lee started just after Irene at the beginning of September 2011, in this study we consider them as a single contiguous hurricane event. The data set used for this study is generated by the New York Harbor Observing and Prediction System (NYHOPS). We find that even the simplest possible way of creating an ensemble produces results superior to any single forecast. We also show that the ensemble models we propose generally have better performance compared to the simple average ensemble technique.

Keywords: Bayesian learning, ensemble model, statistical analysis, storm surge prediction

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Authors: S. Mishra, J. Monro, H. Edwards, J. Podd

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When a fruit such as kiwifruit is consumed its tissues are released from the physical /anatomical constraints existing in the fruit. During digestion they may expand several-fold to achieve a hydrated solids volume far greater than the original fruit, and occupy the available space in the gut, where they surround and interact with other food components. Within the cell wall dispersion, in vitro digestion of co-consumed carbohydrate, diffusion of digestion products, and mixing responsible for mass transfer of nutrients to the gut wall for absorption, were all retarded. All of the foregoing processes may be involved in the glycaemic response to carbohydrate foods consumed with kiwifruit, such as breakfast cereal. To examine their combined role in reducing the glycaemic response to wheat cereal consumed with kiwifruit we formulated diets containing equal amounts of breakfast cereal, with the addition of either kiwifruit, or sugars of the same composition and quantity as in kiwifruit. Therefore, the only difference between the diets was the presence of non-digestible fruit residues. The diet containing the entire disperse kiwifruit significantly reduced the glycaemic response amplitude and the area under the 0-120 min incremental blood glucose response curve (IAUC), compared with the equicarbohydrate diet containing the added kiwifruit sugars. It also slightly but significantly increased the 120-180 min IAUC by preventing a postprandial overcompensation, indicating improved homeostatic blood glucose control. In a subsequent study in which we used kiwifruit in a carbohydrate exchange format, in which the kiwifruit carbohydrate partially replaced breakfast cereal in equal carbohydrate meals, the blood glucose was further reduced without a loss of satiety, and with a reduction in insulin demand. The results show that kiwifruit may be a valuable component in low glycaemic impact diets.

Keywords: carbohydrate, digestion, glycaemic response, kiwifruit

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Authors: Worawanna Panyakotkaew, Falan Srisuriyachai

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Cyclic steam injection (CSI) is a thermal recovery technique performed by injecting periodically heated steam into heavy oil reservoir. Oil viscosity is substantially reduced by means of heat transferred from steam. Together with gas pressurization, oil recovery is greatly improved. Nevertheless, prediction of effectiveness of the process is difficult when reservoir contains degree of heterogeneity. Therefore, study of heterogeneity together with interest reservoir properties must be evaluated prior to field implementation. In this study, thermal reservoir simulation program is utilized. Reservoir model is firstly constructed as multi-layered with coarsening upward sequence. The highest permeability is located on top layer with descending of permeability values in lower layers. Steam is injected from two wells located diagonally in quarter five-spot pattern. Heavy oil is produced by adjusting operating parameters including soaking period and steam quality. After selecting the best conditions for both parameters yielding the highest oil recovery, effects of degree of heterogeneity (represented by Lorenz coefficient), vertical permeability and permeability sequence are evaluated. Surprisingly, simulation results show that reservoir heterogeneity yields benefits on CSI technique. Increasing of reservoir heterogeneity impoverishes permeability distribution. High permeability contrast results in steam intruding in upper layers. Once temperature is cool down during back flow period, condense water percolates downward, resulting in high oil saturation on top layers. Gas saturation appears on top after while, causing better propagation of steam in the following cycle due to high compressibility of gas. Large steam chamber therefore covers most of the area in upper zone. Oil recovery reaches approximately 60% which is of about 20% higher than case of heterogeneous reservoir. Vertical permeability exhibits benefits on CSI. Expansion of steam chamber occurs within shorter time from upper to lower zone. For fining upward permeability sequence where permeability values are reversed from the previous case, steam does not override to top layers due to low permeability. Propagation of steam chamber occurs in middle of reservoir where permeability is high enough. Rate of oil recovery is slower compared to coarsening upward case due to lower permeability at the location where propagation of steam chamber occurs. Even CSI technique produces oil quite slowly in early cycles, once steam chamber is formed deep in the reservoir, heat is delivered to formation quickly in latter cycles. Since reservoir heterogeneity is unavoidable, a thorough understanding of its effect must be considered. This study shows that CSI technique might be one of the compatible solutions for highly heterogeneous reservoir. This competitive technique also shows benefit in terms of heat consumption as steam is injected periodically.

Keywords: cyclic steam injection, heterogeneity, reservoir simulation, thermal recovery

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Authors: C. Malça, P. Gonçalves, N. Alves, A. Mateus

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Regardless of the manufacturing process used, subtractive or additive, material, purpose and application, produced components are conventionally solid mass with more or less complex shape depending on the production technology selected. Aspects such as reducing the weight of components, associated with the low volume of material required and the almost non-existent material waste, speed and flexibility of production and, primarily, a high mechanical strength combined with high structural performance, are competitive advantages in any industrial sector, from automotive, molds, aviation, aerospace, construction, pharmaceuticals, medicine and more recently in human tissue engineering. Such features, properties and functionalities are attained in metal components produced using the additive technique of Rapid Prototyping from metal powders commonly known as Selective Laser Melting (SLM), with optimized internal topologies and varying densities. In order to produce components with high strength and high structural and functional performance, regardless of the type of application, three different internal topologies were developed and analyzed using numerical computational tools. The developed topologies were numerically submitted to mechanical compression and four point bending testing. Finite Element Analysis results demonstrate how different internal topologies can contribute to improve mechanical properties, even with a high degree of porosity relatively to fully dense components. Results are very promising not only from the point of view of mechanical resistance, but especially through the achievement of considerable variation in density without loss of structural and functional high performance.

Keywords: additive manufacturing, internal topologies, porosity, rapid prototyping, selective laser melting

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Authors: Gloria Pinilla, Jose Manuel Baena, Patricia Gálvez-Martín, Juan Antonio Marchad

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Cartilage is a dense connective tissue with limited self-repair properties. Currently, the therapeutic use of autologous or allogenic chondrocytes makes up an alternative therapy to the pharmacological treatment. The design of a bioprinted 3D cartilage with chondrocytes and biodegradable biomaterials offers a new therapeutic alternative able of bridging the limitations of current therapies in the field. We have developed an enhanced printing processes-Injection Volume Filling (IVF) to increase the viability and survival of the cells when working with high-temperature thermoplastics without the limitation of the scaffold geometry in contact with cells. We have demonstrated the viability of the printing process using chondrocytes for cartilage regeneration. This development will accelerate the clinical uptake of the technology and overcomes the current limitation when using thermoplastics as scaffolds. An alginate-based hydrogel combined with human chondrocytes (isolated from osteoarthritis patients) was formulated as bioink-A and the polylactic acid as bioink-B. The bioprinting process was carried out with the REGEMAT V1 bioprinter (Regemat 3D, Granada-Spain) through a IVF. The printing capacity of the bioprinting plus the viability and cell proliferation of bioprinted chondrociytes was evaluated after five weeks by confocal microscopy and Alamar Blue Assay (Biorad). Results showed that the IVF process does not decrease the cell viability of the chondrocytes during the printing process as the cells do not have contact with the thermoplastic at elevated temperatures. The viability and cellular proliferation of the bioprinted artificial 3D cartilage increased after 5 weeks. In conclusion, this study demonstrates the potential use of Regemat V1 for 3D bioprinting of cartilage and the viability of bioprinted chondrocytes in the scaffolds for application in regenerative medicine.

Keywords: cartilage regeneration, bioprinting, bioink, scaffold, chondrocyte

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Authors: Seema Dhariwal, Kiran Maan, Ruchi Baghel, Apoorva Sharma, Poonam Rana

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Introduction: Traumatic brain injury (TBI) is defined as the temporary or permanent alteration in brain function and pathology caused by an external mechanical force. It represents the leading cause of mortality and morbidity among children and youth individuals. Various models of TBI in rodents have been developed in the laboratory to mimic the scenario of injury. Blast overpressure injury is common among civilians and military personnel, followed by accidents or explosive devices. In addition to this, the lateral Controlled cortical impact (CCI) model mimics the blunt, penetrating injury. Method: In the present study, we have developed two different mild TBI models using blast and CCI injury. In the blast model, helium gas was used to create an overpressure of 130 kPa (±5) via a shock tube, and CCI injury was induced with an impact depth of 1.5mm to create diffusive and focal injury, respectively. C57BL/6J male mice (10-12 weeks) were divided into three groups: (1) control, (2) Blast treated, (3) CCI treated, and were exposed to different injury models. Serum was collected on Day1 and day7, followed by biphasic extraction using MTBE/Methanol/Water. Prepared samples were separated on Charged Surface Hybrid (CSH) C18 column and acquired on UHPLC-Q-TOF-MS using ESI probe with inhouse optimized parameters and method. MS peak list was generated using Markerview TM. Data were normalized, Pareto-scaled, and log-transformed, followed by multivariate and univariate analysis in metaboanalyst. Result and discussion: Untargeted profiling of lipids generated extensive data features, which were annotated through LIPID MAPS® based on their m/z and were further confirmed based on their fragment pattern by LipidBlast. There is the final annotation of 269 features in the positive and 182 features in the negative mode of ionization. PCA and PLS-DA score plots showed clear segregation of injury groups to controls. Among various lipids in mild blast and CCI, five lipids (Glycerophospholipids {PC 30:2, PE O-33:3, PG 28:3;O3 and PS 36:1 } and fatty acyl { FA 21:3;O2}) were significantly altered in both injury groups at Day 1 and Day 7, and also had VIP score >1. Pathway analysis by Biopan has also shown hampered synthesis of Glycerolipids and Glycerophospholipiods, which coincides with earlier reports. It could be a direct result of alteration in the Acetylcholine signaling pathway in response to TBI. Understanding the role of a specific class of lipid metabolism, regulation and transport could be beneficial to TBI research since it could provide new targets and determine the best therapeutic intervention. This study demonstrates the potential lipid biomarkers which can be used for injury severity diagnosis and identification irrespective of injury type (diffusive or focal).

Keywords: LipidBlast, lipidomic biomarker, LIPID MAPS®, TBI

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Authors: Katarzyna Bialik-Wąs, Klaudia Pluta, Dagmara Malina, Małgorzata Miastkowska

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In recent years, biocompatible hydrogels have been used more and more in medical applications, especially as modern dressings and drug delivery systems. The main goal of this research was the characteristics of bio-hybrid hydrogel materials incorporated with the nanocarrier-drug system, which enable the release in a gradual and prolonged manner, up to 7 days. Therefore, the use of such a combination will provide protection against mechanical damage and adequate hydration. The proposed bio-hybrid hydrogels are characterized by: transparency, biocompatibility, good mechanical strength, and the dual release system, which allows for gradual delivery of the active substance, even up to 7 days. Bio-hybrid hydrogels based on sodium alginate (SA), poly(vinyl alcohol) (PVA), glycerine, and Aloe vera solution (AV) were obtained through the chemical crosslinking method using poly(ethylene glycol) diacrylate as a crosslinking agent. Additionally, a nanocarrier-drug system was incorporated into SA/PVA/AV hydrogel matrix. Here, studies were focused on the release profiles of active substances from bio-hybrid hydrogels using the USP4 method (DZF II Flow-Through System, Erweka GmbH, Langen, Germany). The equipment incorporated seven in-line flow-through diffusion cells. The membrane was placed over support with an orifice of 1,5 cm in diameter (diffusional area, 1.766 cm²). All the cells were placed in a cell warmer connected with the Erweka heater DH 2000i and the Erweka piston pump HKP 720. The piston pump transports the receptor fluid via seven channels to the flow-through cells and automatically adapts the setting of the flow rate. All volumes were measured by gravimetric methods by filling the chambers with Milli-Q water and assuming a density of 1 g/ml. All the determinations were made in triplicate for each cell. The release study of the model active substance was carried out using a regenerated cellulose membrane Spectra/Por®Dialysis Membrane MWCO 6-8,000 Carl Roth® Company. These tests were conducted in buffer solutions – PBS at pH 7.4. A flow rate of receptor fluid of about 4 ml /1 min was selected. The experiments were carried out for 7 days at a temperature of 37°C. The released concentration of the model drug in the receptor solution was analyzed using UV-Vis spectroscopy (Perkin Elmer Company). Additionally, the following properties of the modified materials were studied: physicochemical, structural (FT-IR analysis), morphological (SEM analysis). Finally, the cytotoxicity tests using in vitro method were conducted. The obtained results exhibited that the dual release system allows for the gradual and prolonged delivery of the active substances, even up to 7 days.

Keywords: wound dressings, SA/PVA hydrogels, nanocarrier-drug system, USP4 method

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Authors: Shraddha Jain Sharma, Ratnalata Gupta

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Predicting stock movements in the financial market is both difficult and rewarding. Analysts and academics are increasingly using advanced approaches such as machine learning techniques to anticipate stock price patterns, thanks to the expanding capacity of computing and the recent advent of graphics processing units and tensor processing units. Stock market prediction is a type of time series prediction that is incredibly difficult to do since stock prices are influenced by a variety of financial, socioeconomic, and political factors. Furthermore, even minor mistakes in stock market price forecasts can result in significant losses for companies that employ the findings of stock market price prediction for financial analysis and investment. Soft computing techniques are increasingly being employed for stock market prediction due to their better accuracy than traditional statistical methodologies. The proposed research looks at the need for soft computing techniques in stock market prediction, the numerous soft computing approaches that are important to the field, past work in the area with their prominent features, and the significant problems or issue domain that the area involves. For constructing a predictive model, the major focus is on neural networks and fuzzy logic. The stock market is extremely unpredictable, and it is unquestionably tough to correctly predict based on certain characteristics. This study provides a complete overview of the numerous strategies investigated for high accuracy prediction, with a focus on the most important characteristics.

Keywords: stock market prediction, artificial intelligence, artificial neural networks, fuzzy logic, accuracy, deep learning, machine learning, stock price, trading volume

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Authors: Al Sayed Al Soudy, Valeria Maselli, Gianluca Polese, Anna Di Cosmo

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Cephalopods are considered as a model organism with a rich behavioral repertoire. Sophisticated behaviors were widely studied and described in different species such as Octopus vulgaris, who has evolved the largest and more complex nervous system among invertebrates. In O. vulgaris, cognitive abilities in problem-solving tasks and learning abilities are associated with long-term memory and spatial memory, mediated by highly developed sensory organs. They are equipped with sophisticated eyes, able to discriminate colors even with a single photoreceptor type, vestibular system, ‘lateral line analogue’, primitive ‘hearing’ system and olfactory organs. They can recognize chemical cues either through direct contact with odors sources using suckers or by distance through the olfactory organs. Cephalopods are able to detect widespread waterborne molecules by the olfactory organs. However, many volatile odorant molecules are insoluble or have a very low solubility in water, and must be perceived by direct contact. O. vulgaris, equipped with many chemosensory neurons located in their suckers, exhibits a peculiar behavior that can be provocatively described as 'smell by touch'. The aim of this study is to establish the priority given to chemical vs. visual perception in food choice. Materials and methods: Three different types of food (anchovies, clams, and mussels) were used, and all sessions were recorded with a digital camera. During the acclimatization period, Octopuses were exposed to the three types of food to test their natural food preferences. Later, to verify if food preference is maintained, food was provided in transparent screw-jars with pierced lids to allow both visual and chemical recognition of the food inside. Subsequently, we tested alternatively octopuses with food in sealed transparent screw-jars and food in blind screw-jars with pierced lids. As a control, we used blind sealed jars with the same lid color to verify a random choice among food types. Results and discussion: During the acclimatization period, O. vulgaris shows a higher preference for anchovies (60%) followed by clams (30%), then mussels (10%). After acclimatization, using the transparent and pierced screw jars octopus’s food choices resulted in 50-50 between anchovies and clams, avoiding mussels. Later, guided by just visual sense, with transparent but not pierced jars, their food preferences resulted in 100% anchovies. With pierced but not transparent jars their food preference resulted in 100% anchovies as first food choice, the clams as a second food choice result (33.3%). With no possibility to select food, neither by vision nor by chemoreception, the results were 20% anchovies, 20% clams, and 60% mussels. We conclude that O. vulgaris uses both chemical and visual senses in an integrative way in food choice, but if we exclude one of them, it appears clear that its food preference relies on chemical sense more than on visual perception.

Keywords: food choice, Octopus vulgaris, olfaction, sensory organs, visual sense

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Authors: Teja Sai Puligadda

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Satellite images in detail can serve an important role in the geographic study. Quantitative and qualitative information provided by the satellite and remote sensing images minimizes the complexity of work and time. Data/images are captured at regular intervals by satellite remote sensing systems, and the amount of data collected is often enormous, and it expands rapidly as technology develops. Interpreting remote sensing images, geographic data mining, and researching distinct vegetation types such as agricultural and forests are all part of satellite image categorization. One of the biggest challenge data scientists faces while classifying satellite images is finding the best suitable classification algorithms based on the available that could able to classify images with utmost accuracy. In order to categorize satellite images, which is difficult due to the sheer volume of data, many academics are turning to deep learning machine algorithms. As, the CNN algorithm gives high accuracy in image recognition problems and automatically detects the important features without any human supervision and the ANN algorithm stores information on the entire network (Abhishek Gupta., 2020), these two deep learning algorithms have been used for satellite image classification. This project focuses on remote sensing through Deep Neural Networks i.e., ANN and CNN with Deep Sat (SAT-4) Airborne dataset for classifying images. Thus, in this project of classifying satellite images, the algorithms ANN and CNN are implemented, evaluated & compared and the performance is analyzed through evaluation metrics such as Accuracy and Loss. Additionally, the Neural Network algorithm which gives the lowest bias and lowest variance in solving multi-class satellite image classification is analyzed.

Keywords: artificial neural network, convolutional neural network, remote sensing, accuracy, loss

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Authors: Abobakr Khidir Ziyada, Cecilia Devi Wilfred

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In the present work, a new series of 3-propanenitrile imidazolium-based Room Temperature Ionic Liquids (RTILs), incorporating dioctyl sulfosuccinate (DOSS) were prepared by reacting imidazole with acrylonitrile and then reacting the product with allyl chloride, 2-chloroethanol, and benzyl chloride. After the reaction had been completed, metathesis reaction was carried out using sodium dioctyl sulfosuccinate. The densities and viscosities of the present RTILs were measured at atmospheric pressure at T=293.15 to 353.15 K, the refractive index was measured at T=293.15 to 333.15 K, whereas, the start and decomposition temperatures were determined at heating rate 10°C. min^-1. The thermal expansion coefficient, densities at a range of temperatures and pressures, molecular volume, molar refraction, standard entropy and the lattice energy of these RTILs were also estimated. The present RTILs showed higher densities, similar refractive indices, and higher viscosities compared to the other 1-alkyl-3-propanenitrile imidazolium-based RTILs. The densities of the present synthesized RTILs are lower compared to the other nitrile-functionalized ILs. These present RTILs showed a weak temperature dependence on the thermal expansion coefficients, αp=5.0 × 10^−4 to 7.50 × 10−4 K^-1. Empirical correlations were proposed to represent the present data on the physical properties. The lattice energy for the present RTILs was similar to other nitrile–based imidazolium RTILs. The present RTILs showed very high molar refraction when compared similar RTILs incorporating other anions.

Keywords: dioctyl sulfosuccinate, nitrile ILs, 3-propanenitrile, anion, room temperature ionic liquids, RTIL

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Authors: Nadarajah I. Ramesh

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Much of the research on stochastic point process models for rainfall has focused on Poisson cluster models constructed from either the Neyman-Scott or Bartlett-Lewis processes. The doubly stochastic Poisson process provides a rich class of point process models, especially for fine-scale rainfall modelling. This paper provides an account of recent development on this topic and presents the results based on some of the fine-scale rainfall models constructed from this class of stochastic point processes. Amongst the literature on stochastic models for rainfall, greater emphasis has been placed on modelling rainfall data recorded at hourly or daily aggregation levels. Stochastic models for sub-hourly rainfall are equally important, as there is a need to reproduce rainfall time series at fine temporal resolutions in some hydrological applications. For example, the study of climate change impacts on hydrology and water management initiatives requires the availability of data at fine temporal resolutions. One approach to generating such rainfall data relies on the combination of an hourly stochastic rainfall simulator, together with a disaggregator making use of downscaling techniques. Recent work on this topic adopted a different approach by developing specialist stochastic point process models for fine-scale rainfall aimed at generating synthetic precipitation time series directly from the proposed stochastic model. One strand of this approach focused on developing a class of doubly stochastic Poisson process (DSPP) models for fine-scale rainfall to analyse data collected in the form of rainfall bucket tip time series. In this context, the arrival pattern of rain gauge bucket tip times N(t) is viewed as a DSPP whose rate of occurrence varies according to an unobserved finite state irreducible Markov process X(t). Since the likelihood function of this process can be obtained, by conditioning on the underlying Markov process X(t), the models were fitted with maximum likelihood methods. The proposed models were applied directly to the raw data collected by tipping-bucket rain gauges, thus avoiding the need to convert tip-times to rainfall depths prior to fitting the models. One advantage of this approach was that the use of maximum likelihood methods enables a more straightforward estimation of parameter uncertainty and comparison of sub-models of interest. Another strand of this approach employed the DSPP model for the arrivals of rain cells and attached a pulse or a cluster of pulses to each rain cell. Different mechanisms for the pattern of the pulse process were used to construct variants of this model. We present the results of these models when they were fitted to hourly and sub-hourly rainfall data. The results of our analysis suggest that the proposed class of stochastic models is capable of reproducing the fine-scale structure of the rainfall process, and hence provides a useful tool in hydrological modelling.

Keywords: fine-scale rainfall, maximum likelihood, point process, stochastic model

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Authors: Herke Csongor

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The artificial intelligence (AI) has been used in the United States of America in the decisionmaking process of the criminal justice system for decades. In the field of law, including criminal law, AI can provide serious assistance in decision-making in many places. The paper reviews four main areas where AI still plays a role in the criminal justice system and where it is expected to play an increasingly important role. The first area is the predictive policing: a number of algorithms are used to prevent the commission of crimes (by predicting potential crime locations or perpetrators). This may include the so-called linking hot-spot analysis, crime linking and the predictive coding. The second area is the Big Data analysis: huge amounts of data sets are already opaque to human activity and therefore unprocessable. Law is one of the largest producers of digital documents (because not only decisions, but nowadays the entire document material is available digitally), and this volume can only and exclusively be handled with the help of computer programs, which the development of AI systems can have an increasing impact on. The third area is the criminal statistical data analysis. The collection of statistical data using traditional methods required enormous human resources. The AI is a huge step forward in that it can analyze the database itself, based on the requested aspects, a collection according to any aspect can be available in a few seconds, and the AI itself can analyze the database and indicate if it finds an important connection either from the point of view of crime prevention or crime detection. Finally, the use of AI during decision-making in both investigative and judicial fields is analyzed in detail. While some are skeptical about the future role of AI in decision-making, many believe that the question is not whether AI will participate in decision-making, but only when and to what extent it will transform the current decision-making system.

Keywords: artificial intelligence, international criminal cooperation, planning and organizing of the investigation, risk assessment

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Authors: Joanna Smoluk-Sikorska, Władysława Łuczka

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Organic farming is an important element of sustainable agriculture. It has been developing very dynamically in Poland, especially since Poland’s accession to the EU. Nevertheless, properly functioning organic market is a necessary condition justifying development of organic agriculture. Despite significant improvement, this market in Poland is still in the initial stage of growth. An important element of the market is distribution, especially retail, which offers specified product range to consumers. Therefore, there is a need to investigate retail outlets offering organic food in order to improve functioning of this part of the market. The inquiry research conducted in three types of outlets offering organic food, between 2011 and 2012 in the 8 largest Polish cities, shows that the majority of outlets offer cereals, processed fruit and vegetables as well as spices and the least shops – meat and sausages. The distributors mostly indicate unsatisfactory product range of suppliers as the reason for this situation. The main providers of the outlets are wholesalers, particularly in case of processed products, and in fresh products – organic farms. A very important distribution obstacle is dispersion of producers, which generates high transportation costs and what follows that, high price of organics. In the investigated shops, the most often used price calculation method is a cost method. The majority of the groceries and specialist shops apply margins between 21 and 40%. The margin in specialist outlets is the highest, in regard to the qualified service and advice. In turn, most retail networks declare the margin between 0 and 20%, which is consistent with low-price strategy applied in these shops. Some lacks in the product range of organics and in particular high prices cause that the demand volume is rather low. Therefore there is a need to support certain market actions, e.g. on-farm processing or promotion.

Keywords: organic food, retail, product range, supply sources

Procedia PDF Downloads 279

Authors: Kaling Taki, Rohit Gahlot, Manish Kumar

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Disposal of Sewage Sludge (SS) is a big issue especially in developing nation like India, where there is no control in the dynamicity of SS produced. The present research work demonstrates the potential application of SS amended with varying percentage (0-100%) of Fly Ash (FA) for brick manufacturing as an alternative of SS management. SS samples were collected from Jaspur sewage treatment plant (Ahmedabad, India) and subjected to different preconditioning treatments: (i) atmospheric drying (ii) pulverization (iii) heat treatment in oven (110°C, moisture removal) and muffle furnace (440°C, organic content removal). Geotechnical parameters of the SS were obtained as liquid limit (52%), plastic limit (24%), shrinkage limit (10%), plasticity index (28%), differential free swell index (DFSI, 47%), silt (68%), clay (27%), organic content (5%), optimum moisture content (OMC, 20%), maximum dry density (MDD, 1.55gm/cc), specific gravity (2.66), swell pressure (57kPa) and unconfined compressive strength (UCS, 207kPa). For FA liquid limit, plastic limit and specific gravity was 44%, 0% and 2.2 respectively. Initially, for brick casting pulverized SS sample was heat treated in a muffle furnace around 440℃ (5 hours) for removal of organic matter. Later, mixing of SS, FA and water by weight ratio was done at OMC. 7*7*7 cm3 sample mold was used for casting bricks at MDD. Brick samples were then first dried in room temperature for 24 hours, then in oven at 100℃ (24 hours) and finally firing in muffle furnace for 1000℃ (10 hours). The fired brick samples were then cured for 3 days according to Indian Standards (IS) common burnt clay building bricks- specification (5th revision). The Compressive strength of brick samples (0, 10, 20, 30, 40, 50 ,60, 70, 80, 90, 100%) of FA were 0.45, 0.76, 1.89, 1.83, 4.02, 3.74, 3.42, 3.19, 2.87, 0.78 and 4.95MPa when evaluated through compressive testing machine (CTM) for a stress rate of 14MPa/min. The highest strength was obtained at 40% FA mixture i.e. 4.02MPa which is much higher than the pure SS brick sample. According to IS 1077: 1992 this combination gives strength more than 3.5 MPa and can be utilized as common building bricks. The loss in weight after firing was much higher than the oven treatment, this might be due to degradation temperature higher than 100℃. The thermal conductivity of the fired brick was obtained as 0.44Wm-1K-1, indicating better insulation properties than other reported studies. TCLP (Toxicity characteristic leaching procedure) test of Cr, Cu, Co, Fe and Ni in raw SS was found as 69, 70, 21, 39502 and 47 mg/kg. The study positively concludes that SS and FA at optimum ratio can be utilized as common building bricks such as partitioning wall and other small strength requirement works. The uniqueness of the work is it emphasizes on utilization of FA for stabilizing SS as construction material as a replacement of natural clay as reported in existing studies.

Keywords: Compressive strength, Curing, Fly Ash, Sewage Sludge.

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Authors: R. Kerbachi, S. Chikhi, M. Boughedaoui

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The study presents an analysis of the Algerian vehicle fleet and resultant emissions. The emission measurement of air pollutants emitted by road transportation (CO, THC, NOX and CO2) was conducted on 17 light duty vehicles in real traffic. This sample is representative of the Algerian light vehicles in terms of fuel quality (gasoline, diesel and liquefied petroleum gas) and the technology quality (injection system and emission control). The experimental measurement methodology of unit emission of vehicles in real traffic situation is based on the use of the mini-Constant Volume Sampler for gas sampling and a set of gas analyzers for CO2, CO, NOx and THC, with an instrumentation to measure kinematics, gas temperature and pressure. The apparatus is also equipped with data logging instrument and data transfer. The results were compared with the database of the European light vehicles (Artemis). It was shown that the technological injection liquefied petroleum gas (LPG) has significant impact on air pollutants emission. Therefore, with the exception of nitrogen oxide compounds, uncatalyzed LPG vehicles are more effective in reducing emissions unit of air pollutants compared to uncatalyzed gasoline vehicles. LPG performance seems to be lower under real driving conditions than expected on chassis dynamometer. On the other hand, the results show that uncatalyzed gasoline vehicles emit high levels of carbon monoxide, and nitrogen oxides. Overall, and in the absence of standards in Algeria, unit emissions are much higher than Euro 3. The enforcement of pollutant emission standard in developing countries is an important step towards introducing cleaner technology and reducing vehicular emissions.

Keywords: on-board measurements of unit emissions of CO, HC, NOx and CO2, light vehicles, mini-CVS, LPG-fuel, artemis, Algeria

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Authors: Muhammad S. Sajid, Asim Shamim, Muhammad Nisar Khan, Ashfaq A. Chatta, Muhammad Saqib

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Goats (Capra hircus) are a valued asset for resource poor farmers globally. But the parasitic infection especially Haemonchus contortus (Trichostrongylid), impact the health and production of goats globally. The present study intended to evaluate resilient and resistance to Haemonchus contortus in indigenous goat breeds (Teddy and Beetal) of Punjab, Pakistan. Out of 60, 30 goats of each breed were divided into 6 groups and each group contain five goats. Two group of each breed received challenged infection with 12000 and 18000 L3 (third stage) larvae of Haemonchus contortus under two infection protocol that is early and trickle and remaining two group of each breed was kept as control. Resilient and resistance of each breed was then measured on the basis of their phenotypic markers like: faecal egg counts, packed cell volume, FAMACHA score system, body weight, total protein, albumin and worm count on 2nd, 4th, 6th, and 8th week of post infection. Variation in response of each goat breeds to Haemonchus contortus was observed. Teddy breed showed significant (P < 0.05)resistance as compared to Beetal. It is probably first attempt to report an evaluation of goat breed response towards Haemonchus contortus in Pakistan. It was concluded that Teddy goats have a greater genetic tendency to resist against to the Haemonchus contortus infection and this breed could be kept and bred from the economic point of view. Evaluation of genetic markers are like: gene, protein expression, Immunoglobulin, Histamines and interleukins determination are recommended for future studies which can be helpful to be fined resistant breed of goats.

Keywords: goat, beetal, teddy, haemonchus contortus, resistance, resilience, phenotypic markers

Procedia PDF Downloads 348