Search results for: genetic breeding models

Authors: John Deely, Stephen Hynes, Mary Cawley, Sarah Hogan

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Domestic marine and coastal tourism have increased in importance over the last number of years due to the impacts of international travel, environmental concerns, associated health benefits and COVID-19 related travel restrictions. Consequently, this paper conceptualizes domestic marine and coastal tourism within an economic framework. Two logit models examine the factors that influence participation in the coastal day trips and overnight stays markets, respectively. Two truncated travel cost models are employed to explore trip duration, one analyzing the number of day trips taken and the other examining the number of nights spent in marine and coastal areas. Although a range of variables predicts participation, no one variable had a significant and consistent effect on every model. A division in access to domestic marine and coastal tourism is also observed based on variation in household income. The results also indicate a vibrant day trip market and large consumer surpluses.

Keywords: domestic marine and coastal tourism, day tripper, participation models, truncated travel cost model

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Authors: Gaurav Kumar Sinha

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The volatility of the oil market, influenced by geopolitical, economic, and environmental factors, presents significant challenges for stakeholders in predicting trends and demand. This article explores the application of artificial intelligence (AI) in developing robust forecasting models to anticipate changes in the oil market more accurately. We delve into various AI techniques, including machine learning, deep learning, and time series analysis, that have been adapted to analyze historical data and current market conditions to forecast future trends. The study evaluates the effectiveness of these models in capturing complex patterns and dependencies in market data, which traditional forecasting methods often miss. Additionally, the paper discusses the integration of external variables such as political events, economic policies, and technological advancements that influence oil prices and demand. By leveraging AI, stakeholders can achieve a more nuanced understanding of market dynamics, enabling better strategic planning and risk management. The article concludes with a discussion on the potential of AI-driven models in enhancing the predictive accuracy of oil market forecasts and their implications for global economic planning and strategic resource allocation.

Keywords: AI forecasting, oil market trends, machine learning, deep learning, time series analysis, predictive analytics, economic factors, geopolitical influence, technological advancements, strategic planning

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Authors: Hafizuddin W. Yussof, Syamsutajri S. Bahri, Adam P. Harvey

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Strong anion exchange resins with QN+OH-, have the potential to be developed and employed as heterogeneous catalyst for transesterification, as they are chemically stable to leaching of the functional group. Nine different SIERs (SIER1-9) with QN+OH- were prepared by suspension polymerization of vinylbenzyl chloride-divinylbenzene (VBC-DVB) copolymers in the presence of n-heptane (pore-forming agent). The amine group was successfully grafted into the polymeric resin beads through functionalization with trimethylamine. These SIERs are then used as a catalyst for the transesterification of triacetin with methanol. A set of differential equations that represents the Langmuir-Hinshelwood-Hougen-Watson (LHHW) and Eley-Rideal (ER) models for the transesterification reaction were developed. These kinetic models of LHHW and ER were fitted to the experimental data. Overall, the synthesized ion exchange resin-catalyzed reaction were well-described by the Eley-Rideal model compared to LHHW models, with sum of square error (SSE) of 0.742 and 0.996, respectively.

Keywords: anion exchange resin, Eley-Rideal, Langmuir-Hinshelwood-Hougen-Watson, transesterification

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Authors: E. Al Daoud

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In many cases, theoretical treatments are available for models for which there is no perfect physical realization. In this situation, the only possible test for an approximate theoretical solution is to compare with data generated from a computer simulation. In this paper, Monte Carlo tools are used to study and compare the elementary particles models. All the experiments are implemented using 10000 events, and the simulated energy is 13 TeV. The mean and the curves of several variables are calculated for each model using MadAnalysis 5. Anomalies in the results can be seen in the muons masses of the minimal supersymmetric standard model and the two Higgs doublet model.

Keywords: Feynman rules, hadrons, Lagrangian, Monte Carlo, simulation

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Authors: Aadityaa Singla

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The big five personality traits are as follows: openness, conscientiousness, extraversion, agreeableness, and neuroticism. In order to get an insight into their personality, many flocks to these categories, which each have different meanings/characteristics. This information is important not only to individuals but also to career professionals and psychologists who can use this information for candidate assessment or job recruitment. The links between AI and psychology have been well studied in cognitive science, but it is still a rather novel development. It is possible for various AI classification models to accurately predict a personality question via ten input questions. This would contrast with the hundred questions that normal humans have to answer to gain a complete picture of their five personality traits. In order to approach this problem, various AI classification models were used on a dataset to predict what a user may answer. From there, the model's prediction was compared to its actual response. Normally, there are five answer choices (a 20% chance of correct guess), and the models exceed that value to different degrees, proving their significance. By utilizing an MLP classifier, decision tree, linear model, and K-nearest neighbors, they were able to obtain a test accuracy of 86.643, 54.625, 47.875, and 52.125, respectively. These approaches display that there is potential in the future for more nuanced predictions to be made regarding personality.

Keywords: machine learning, personally, big five personality traits, cognitive science

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Authors: Abejide Dorcas Ropo, Falusi Olamide Ahmed, Daudu Oladipupo Abdulazeez Yusuf, Muhammad Liman Muhammad, Gado Aishatu Adamu

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Bambara groundnut is an indigenous African legume with great potential to tackle the problem of food insecurity in Nigeria. A germplasm collection mission was carried out in collaboration with the Agricultural Developments Project (ADP) Extension officers of Nigeria between October and December 2014. Bambara groundnut seeds were collected from farmers in different States in Nigeria, such as Kaduna, Niger, Kogi, Benue, Plateau, Adamawa, Nasarawa, Jigawa, Enugu, and Federal Capital Territoy (FCT) Abuja. Some seeds were also collected from National Centre for Genetic Resources and Biotechnology (NACGRAB). The seeds were phenotyped using the descriptor list of Vigna subterranea produced by the International Plant Genetic Resource Institute. A total of 45 original seed lots were collected, which comprised of mixed seeds having different seed coat colours (15) and pure seeded accessions having the same seed coat and eye colour (30). After sorting, a total of 83 accessions were derived from the 45 original seed lots collected, and a total of 24 distinct seed morphotypes with varying seed coat colours and eye colours were identified from the collections. They include cream ( cream ash eye, cream plain eye, and cream black eye), cream purplish spots, cream brown spots/stripe, cream black stripe, cream dark brown patches, cream light grey spots, cream black patches, black, red, light red, dark red, brownish red, brown speckled with black, red speckled with black, brown, brown with brown pattern below hilum, brown with black pattern below hilum, cream black, grey brown, grey black and variegated red. The highest number of accessions were collected from NACGRAB (11), followed by Niger State (10), and the lowest from Benue, Jigawa, and Adamawa States (2). Niger State also had the highest number of mixed seeds. The different seed phenotypes observed in the study are important for the field production of true-to-type lines and can be exploited for the genetic improvement of the Bambara groundnut.

Keywords: Bambara groundnut, characterization, collection, germplasm, phenotypic

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Authors: B. Sir, M. Podhoranyi, S. Kuchar, T. Kocyan

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Rainfall-runoff models play important role in hydrological predictions. However, the model is only one part of the process for creation of flood prediction. The aim of this paper is to show the process of successful prediction for flood event (May 15–May 18 2014). The prediction was performed by rainfall runoff model HEC–HMS, one of the models computed within Floreon+ system. The paper briefly evaluates the results of automatic hydrologic prediction on the river Olše catchment and its gages Český Těšín and Věřňovice.

Keywords: flood, HEC-HMS, prediction, rainfall, runoff

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Authors: Moulana Mohammed

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Topic models are widely used in building clusters of documents for more than a decade, yet problems occurring in choosing optimal number of topics. The main problem is the lack of a stable metric of the quality of topics obtained during the construction of topic models. The authors analyzed from previous works, most of the models used in determining the number of topics are non-parametric and quality of topics determined by using perplexity and coherence measures and concluded that they are not applicable in solving this problem. In this paper, we used the parametric method, which is an extension of the traditional topic model with visual access tendency for visualization of the number of topics (clusters) to complement clustering and to choose optimal number of topics based on results of cluster validity indices. Developed hybrid topic models are demonstrated with different Twitter datasets on various topics in obtaining the optimal number of topics and in measuring the quality of clusters. The experimental results showed that the Visual Non-negative Matrix Factorization (VNMF) topic model performs well in determining the optimal number of topics with interactive visualization and in performance measure of the quality of clusters with validity indices.

Keywords: interactive visualization, visual mon-negative matrix factorization model, optimal number of topics, cluster validity indices, Twitter data clustering

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Authors: Fabienne Vozy, Anick Lepage

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Insects are displaying high nutritional value, low greenhouse gas emissions, low land use requirements and high food conversion efficiency. They can contribute to the food chain and be one of many solutions to protein shortages. Currently, in North America, nutritional entomology is under-developed and the needs to better understand its benefits remain to convince large-scale producers and consumers (both for human and agricultural needs). As such, large-scale production of mealworms offers a promising alternative to replacing traditional sources of protein and fatty acids. To proceed orderly, it is required to collect more data on the nutritional values of insects such as, a) Evaluate the diets of insects to improve their dietary value; b) Test the breeding conditions to optimize yields; c) Evaluate the use of by-products and organic residues as sources of food. Among the featured technical parameters, relative humidity (RH) percentage and temperature, optimal substrates and hydration sources are critical elements, thus establishing potential benchmarks for to optimize conversion rates of protein and fatty acids. This research is to establish the combination of the most influential rearing parameters with local food residues, to correlate the findings with the nutritional value of the larvae harvested. 125 same-monthly old adults/replica are randomly selected in the mealworm breeding pool then placed to oviposit in growth chambers preset at 26°C and 65% RH. Adults are removed after 7 days. Larvae are harvested upon the apparition of the first nymphosis signs and batches, are analyzed for their nutritional values using wet chemistry analysis. The first samples analyses include total weight of both fresh and dried larvae, residual humidity, crude proteins (CP%), and crude fats (CF%). Further analyses are scheduled to include soluble proteins and fatty acids. Although they are consistent with previous published data, the preliminary results show no significant differences between treatments for any type of analysis. Nutritional properties of each substrate combination have yet allowed to discriminate the most effective residue recipe. Technical issues such as the particles’ size of the various substrate combinations and larvae screen compatibility are to be investigated since it induced a variable percentage of lost larvae upon harvesting. To address those methodological issues are key to develop a standardized efficient procedure. The aim is to provide producers with easily reproducible conditions, without incurring additional excessive expenditure on their part in terms of equipment and workforce.

Keywords: entomophagy, nutritional value, rearing parameters optimization, Tenebrio molitor

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Authors: M. Pepe, S. Ackermann, L. Fregonese, C. Achille

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3D models obtained with advanced survey techniques such as close-range photogrammetry and laser scanner are nowadays particularly appreciated in Cultural Heritage and Archaeology fields. In order to produce high quality models representing archaeological evidences and anthropological artifacts, the appearance of the model (i.e. color) beyond the geometric accuracy, is not a negligible aspect. The integration of the close-range photogrammetry survey techniques with the laser scanner is still a topic of study and research. By combining point cloud data sets of the same object generated with both technologies, or with the same technology but registered in different moment and/or natural light condition, could construct a final point cloud with accentuated color dissimilarities. In this paper, a methodology to uniform the different data sets, to improve the chromatic quality and to highlight further details by balancing the point color will be presented.

Keywords: color models, cultural heritage, laser scanner, photogrammetry

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Authors: Nadeem Munawar, Tariq Mahmood, Paula Rivadeneira, Ali Akhter

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In this paper, we review on rodent species which are common inhabitants of agricultural landscapes where they are an important prey source for a wide variety of avian, reptilian, and mammalian predators. Agricultural fields are surrounded by fallow land, which provide suitable sites for shelter and breeding for rodents, while shrubs, grasses, annual weeds and forbs may provide supplementary food. The assemblage of rodent’s fauna in the cropland habitats including cropped fields, meadows and adjacent field structures like hedgerows, woodland and field margins fluctuates seasonally. The mature agricultural crops provides good source of food and shelter to the rodents and these factors along with favorable climatic factors/season facilitate breeding activities of these rodent species. Changes in vegetation height and vegetative cover affect two important aspects of a rodent’s life: food and shelter. In addition, during non-crop period vegetation can be important for building nests above or below ground and it provides thermal protection for rodents from heat and cold. The review revealed that rodents form a very diverse group of mammals, ranging from tiny pigmy mice to big capybaras, from arboreal flying squirrels to subterranean mole rats, from opportunistic omnivores (e.g. Norway rats) to specialist feeders (e.g. the North African fat sand rats that feed on a single family of plants only). It is therefore no surprise that some species thrive well under the conditions that are found in agricultural fields. The review on the population dynamics of the rodent species indicated that they are agricultural pests probably due to the heterogeneous landscape and to the high rotativity of vegetable crop cultivation. They also cause damage to various crops, directly and indirectly, by gnawing, spoilage, contamination and hoarding activities, besides this behavior they have also significance importance in agricultural habitat. The burrowing activities of rodents alter the soil properties around their burrows which improve its aeration, infiltration, increase the water holding capacity and thus encourage plant growth. These properties are beneficial for the soil because they affect absorption of phosphorus, absorption zinc, copper, other nutrients and the uptake of water and thus rodents are known as indicator species in agricultural fields. Our review suggests that wide crop field’s borders, particularly those contiguous to various cropland fields, should be understood as priority sites for nesting, feeding, and cover for the rodent’s fauna. The goal of this review paper is to provide a comprehensive synthesis of understanding regarding rodent habitat and biodiversity in agricultural landscapes.

Keywords: agricultural landscapes, food, indicator species, shelter

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Authors: Supriya Ambawat, Subaran Singh, C. Tara Satyavathi, B. S. Rajpurohit, Ummed Singh, Balraj Singh

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Pearl millet [Pennisetum glaucum (L.) R. Br.] is an important staple food of the arid and semiarid tropical regions of Asia, Africa, and Latin America. It is rightly termed as nutricereal as it has high nutrition value and a good source of carbohydrate, protein, fat, ash, dietary fiber, potassium, magnesium, iron, zinc, etc. Pearl millet has low prolamine fraction and is gluten free which is useful for people having a gluten allergy. It has several health benefits like reduction in blood pressure, thyroid, diabe¬tes, cardiovascular and celiac diseases but its direct consumption as food has significantly declined due to several reasons. Keeping this in view, it is important to reorient the ef¬forts to generate demand through value-addition and quality improvement and create awareness on the nutritional merits of pearl millet. In India, through Indian Council of Agricultural Research-All India Coordinated Research Project on Pearl millet, multilocational coordinated trials for developed hybrids were conducted at various centers. The gene banks of pearl millet contain varieties with high levels of iron and zinc which were used to produce new pearl millet varieties with elevated iron levels bred with the high‐yielding varieties. Thus, using breeding approaches and biochemical analysis, a total of 167 hybrids and 61 varieties were identified and released for cultivation in different agro-ecological zones of the country which also includes some biofortified hybrids rich in Fe and Zn. Further, using several biotechnological interventions such as molecular markers, next-generation sequencing (NGS), association mapping, nested association mapping (NAM), MAGIC populations, genome editing, genotyping by sequencing (GBS), genome wide association studies (GWAS) advancement in millet improvement has become possible by identifying and tagging of genes underlying a trait in the genome. Using DArT markers very high density linkage maps were constructed for pearl millet. Improved HHB67 has been released using marker assisted selection (MAS) strategies, and genomic tools were used to identify Fe-Zn Quantitative Trait Loci (QTL). The draft genome sequence of millet has also opened various ways to explore pearl millet. Further, genomic positions of significantly associated simple sequence repeat (SSR) markers with iron and zinc content in the consensus map is being identified and research is in progress towards mapping QTLs for flour rancidity. The sequence information is being used to explore genes and enzymatic pathways responsible for rancidity of flour. Thus, development and application of several biotechnological approaches along with biofortification can accelerate the genetic gain targets for pearl millet improvement and help improve its quality.

Keywords: Biotechnological approaches, genomic tools, malnutrition, MAS, nutricereal, pearl millet, sequencing.

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Authors: Bhavita S. Dave, Jaimin Vaidya, Chandresh H. Solanki, Atul K.

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To measure the permeability of a soil specimen, one of the basic assumptions of Darcy's law is that the soil sample should be saturated. Unlike saturated soils, the permeability of unsaturated soils cannot be found using conventional methods as it does not follow Darcy's law. Many empirical models, such as the Van Genuchten Model and Fredlund-Xing Model were suggested to predict permeability value for unsaturated soil. Such models use data from the soil-freezing characteristic curve to find fitting parameters for frozen unsaturated soils. In this study, soil specimens were subjected to 0, 1, 3, and 5 freezing-thawing (F-T) cycles for different degrees of saturation to have a wide range of suction, and its soil freezing characteristic curves were formulated for all F-T cycles. Changes in fitting parameters and relative permeability with subsequent F-T cycles are presented in this paper for both models.

Keywords: frozen unsaturated soil, Fredlund Xing model, soil-freezing characteristic curve, Van Genuchten model

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Authors: Adel M. Mahmoud

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This Investigation was conducted to determine the productivity and water use efficiency for new sunflower genotypes. Ten sunflower genotypes were evaluated under drip irrigation using two treatments of. Results indicate that decreasing the amount of irrigation water from 1500 to 1130 mm/hectar significantly reduced all studied traits. Mutation (M1-63) surpassed all the other one genotypes in seed yield and WUE. Lines which gave the highest yield of the seed have water use efficiency under drought conditions higher than water use efficiency under normal irrigation. The lowest depression in seed yield due to drought conditions has been registered for Line 20, Line M1-63 and Sakha 53 genotypes (11 , 18 and 16 %, respectively). Genotypes (Line 20 , Line M1-63 and Sakha 53) are more tolerant to drought than others and we can used its in breeding program to develop sunflower hybrids suitable for cultivation under drought condition.

Keywords: sunflower genotypes, water use efficiency, mutation, inbred lines

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Authors: O. Behar, A. Khellaf, K. Mohammedi, S. Ait Kaci

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Up to now, most validation studies have been based on the MBE and RMSE, and therefore, focused only on long and short terms performance to test and classify solar radiation models. This traditional analysis does not take into account the quality of modeling and linearity. In our analysis we have tested 22 solar radiation models that are capable to provide instantaneous direct and global radiation at any given location Worldwide. We introduce a new indicator, which we named Global Accuracy Indicator (GAI) to examine the linear relationship between the measured and predicted values and the quality of modeling in addition to long and short terms performance. Note that the quality of model has been represented by the T-Statistical test, the model linearity has been given by the correlation coefficient and the long and short term performance have been respectively known by the MBE and RMSE. An important founding of this research is that the use GAI allows avoiding default validation when using traditional methodology that might results in erroneous prediction of solar power conversion systems performances.

Keywords: solar radiation model, parametric model, performance analysis, Global Accuracy Indicator (GAI)

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Authors: Maria Luisa Nozawa Ribeiro, Maria Teresa Miceli Kerbauy

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This paper aims to present the theories of public participation in order to understand the context of the public GMO (Genetic Modified Organisms) policies in Brazil, highlighting the characteristics of its configuration and the dialog with the experts. As a controversy subject, the commercialization of GMO provoked manifestation of some popular and environmental representative groups questioning the decisions of policy makers and experts on the matter. Many aspects and consequences of the plantation and consumption of this crops emerged and the safety of this technology was questioned. Environmentalists, Civil Right's movement, representatives of rural workers, farmers and organics producers, etc. demonstrated their point of view, also sustained by some experts of medical, genetical, environmental, agronomical sciences, etc. fields. Despite this movement, the precautionary principle (risk management), implemented in 1987, suggested precaution facing new technologies and innovations in the sustainable development society. This principle influenced many legislation and regulation on GMO around the world, including Brazil, which became a reference among the world regulatory GMO systems. The Brazilian legislation ensures the citizens participation on GMO discussion, characteristic that was important to establish the connection between the subject and the participation theory. These deliberation spaces materialized in Brazil through the "Public Audiences", which are managed by the National Biosafety Technical Commission (CTNBio), the department responsible for controlling the research, production and commercialization of GMOs in Brazil.

Keywords: public engagement, public participation, science and technology studies, transgenic politics

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Authors: E. Harrer, M. Mauerhofer, T. Werginz

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As a result of intensive competition, the building sector is suffering from a high degree of rivalry. Furthermore, there can be observed an unbalanced distribution of project risks. Clients are aimed to shift their own risks into the sphere of the constructors or planners. The consequence of this is that the number of conflicts between the involved parties is inordinately high or even increasing; an alternative approach to counter on that developments are cooperative project forms in the construction sector. This research compares conventional contract models and models with partnering agreements to examine the influence on project risks by an early integration of the involved parties. The goal is to show up deviations in different project stages from the design phase to the project transfer phase. These deviations are evaluated by a survey of experts from the three spheres: clients, contractors and planners. By rating the influence of the participants on specific risk factors it is possible to identify factors which are relevant for a smooth project execution.

Keywords: building projects, contract models, partnering, project risks

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Authors: Peter Kress, Alexander Pflaum, Ulrich Loewen

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The number of Internet-of-Things (IoT) platforms is steadily increasing across various industries, especially for smart factories, smart homes and smart mobility. Also in the manufacturing industry, the number of Industrial-IoT platforms is growing. Both IT players, start-ups and increasingly also established industry players and small-and-medium-enterprises introduce offerings for the connection of industrial equipment on platforms, enabled by advanced information and communication technology. Beside the offered functionalities, the established ecosystem of partners around a platform is one of the key differentiators to generate a competitive advantage. The key question is how platform operators design the business model around their platform to attract a high number of customers and partners to co-create value for the entire ecosystem. The present research tries to answer this question by determining the key characteristics of business models of successful platforms in the manufacturing industry. To achieve that, the authors selected an explorative qualitative research approach and created an inductive comparative case study. The authors generated valuable descriptive insights of the business model elements (e.g., value proposition, pricing model or partnering model) of various established platforms. Furthermore, patterns across the various cases were identified to derive propositions for the successful design of business models of platforms in the manufacturing industry.

Keywords: industrial-internet-of-things, business models, platforms, ecosystems, case study

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Authors: Sibusiso Trevor Tshabalala, Samuel Lubbe, Vince Vuledzani Ndou

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Dependency on chemicals has had many disadvantages in pest management control strategies. Such genetic rodenticide resistance and secondary exposure risk are what is currently being experienced. Emphasis on integrated pest management suggests that to control future pests, early intervention and economic threshold development are key starting points in crop production. The significance of this research project is to help establish a relationship between Giant African Land Snail (Achatina Fulica) solution extract, its shell chemical properties, and farmer’s perceptions of biological control in eThekwini Municipality Agri-hubs. A mixed design approach to collecting data will be explored using a trial layout in the field and through interviews. The experimental area will be explored using a split-plot design that will be replicated and arranged in a randomised complete block design. The split-plot will have 0, 10, 20 and 30 liters of water to one liter of snail solution extract. Plots were 50 m² each with a spacing of 12 m between each plot and a plant spacing of 0.5 m (inter-row) ‘and 0.5 m (intra-row). Trials will be irrigated using sprinkler irrigation, with objective two being added to the mix every 4-5 days. The expected outcome will be improved soil fertility and micro-organisms population proliferation.

Keywords: giant african land snail, integrated pest management, photosynthesis, genetic rodenticide resistance, control future pests, shell chemical properties

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Authors: Hazel Pettifor, Charlie Wilson, David Mccollum, Oreane Edelenbosch

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Vehicle purchase is a technology adoption decision that will strongly influence future energy and emission outcomes. Global integrated assessment models (IAMs) provide valuable insights into the medium and long terms effects of socio-economic development, technological change and climate policy. In this paper we present a unique and transparent approach for improving the behavioural representation of these models by incorporating social influence effects to more accurately represent consumer choice. This work draws together strong conceptual thinking and robust empirical evidence to introduce heterogeneous and interconnected consumers who vary in their aversion to new technologies. Focussing on vehicle choice, we conduct novel empirical research to parameterise consumer risk aversion and how this is shaped by social and cultural influences. We find robust evidence for social influence effects, and variation between countries as a function of cultural differences. We then formulate an approach to modelling social influence which is implementable in both simulation and optimisation-type models. We use two global integrated assessment models (IMAGE and MESSAGE) to analyse four scenarios that introduce social influence and cultural differences between regions. These scenarios allow us to explore the interactions between consumer preferences and social influence. We find that incorporating social influence effects into global models accelerates the early deployment of electric vehicles and stimulates more widespread deployment across adopter groups. Incorporating cultural variation leads to significant differences in deployment between culturally divergent regions such as the USA and China. Our analysis significantly extends the ability of global integrated assessment models to provide policy-relevant analysis grounded in real-world processes.

Keywords: behavioural realism, electric vehicles, social influence, vehicle choice

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Authors: Gautier Viaud, Paul-Henry Cournède

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Plant growth models have been used extensively for the prediction of the phenotypic performance of plants. However, they remain most often calibrated for a given genotype and therefore do not take into account genotype by environment interactions. One way of achieving such an objective is to consider Bayesian hierarchical models. Three levels can be identified in such models: The first level describes how a given growth model describes the phenotype of the plant as a function of individual parameters, the second level describes how these individual parameters are distributed within a plant population, the third level corresponds to the attribution of priors on population parameters. Thanks to the Bayesian framework, choosing appropriate priors for the population parameters permits to derive analytical expressions for the full conditional distributions of these population parameters. As plant growth models are of a nonlinear nature, individual parameters cannot be sampled explicitly, and a Metropolis step must be performed. This allows for the use of a hybrid Gibbs--Metropolis sampler. A generic approach was devised for the implementation of both general state space models and estimation algorithms within a programming platform. It was designed using the Julia language, which combines an elegant syntax, metaprogramming capabilities and exhibits high efficiency. Results were obtained for Arabidopsis thaliana on both simulated and real data. An organ-scale Greenlab model for the latter is thus presented, where the surface areas of each individual leaf can be simulated. It is assumed that the error made on the measurement of leaf areas is proportional to the leaf area itself; multiplicative normal noises for the observations are therefore used. Real data were obtained via image analysis of zenithal images of Arabidopsis thaliana over a period of 21 days using a two-step segmentation and tracking algorithm which notably takes advantage of the Arabidopsis thaliana phyllotaxy. Since the model formulation is rather flexible, there is no need that the data for a single individual be available at all times, nor that the times at which data is available be the same for all the different individuals. This allows to discard data from image analysis when it is not considered reliable enough, thereby providing low-biased data in large quantity for leaf areas. The proposed model precisely reproduces the dynamics of Arabidopsis thaliana’s growth while accounting for the variability between genotypes. In addition to the estimation of the population parameters, the level of variability is an interesting indicator of the genotypic stability of model parameters. A promising perspective is to test whether some of the latter should be considered as fixed effects.

Keywords: bayesian, genotypic differentiation, hierarchical models, plant growth models

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Authors: Francesca Croce

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Governmental development strategies aimed at entrepreneurship as a major resource for economic development and poverty reduction of indigenous people. As initiatives and programs are local based, there is a need to better understand the contextual factors of indigenous entrepreneurial models. The purpose of this paper is, therefore, to analyze and integrated the indigenous entrepreneurship literature in order to identify the main models of indigenous entrepreneurship. To answer this need, a systematic literature review was conducted. Relevant articles were identified in selected electronic databases (ABI/Inform Global, Business Source Premier, Web of Science; International Bibliography of the Social Sciences, Academic Search, Sociological Abstract, Entrepreneurial Studies Sources and Bibliography of Native North America) and in selected electronic review. Beginning to 1st January 1995 (first International Day of the World’s Indigenous People), 59 academic articles were selected from 1411. Through systematic analysis of the cultural, social and organizational variables, the paper highlights that a typology of indigenous entrepreneurial models is possible thought the concept of entrepreneurial ecosystem, which includes the geographical position and the environment of the indigenous communities. The results show three models of indigenous entrepreneurship: the urban indigenous entrepreneurship, the semi-urban indigenous entrepreneurship, and rural indigenous entrepreneurship. After the introduction, the paper is organized as follows. In the first part theoretical and practical needs of a systematic literature review on indigenous entrepreneurship are provided. In the second part, the methodology, the selection process and evaluation of the articles are explained. In the third part, findings are presented and each indigenous entrepreneurial model characteristics are discussed. The results of this study bring a new theorization about indigenous entrepreneurship and may be useful for scientists in the field in search of overcoming the cognitive border of Indigenous business models still too little known. Also, the study is addressed to policy makers in charge of indigenous entrepreneurial development strategies more focused on contextual factors studies.

Keywords: community development, entrepreneurial ecosystem, indigenous entrepreneurship model, indigenous people, systematic literature review

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Authors: S. Zith Dey Babu, S. Kour, S. Verma, C. Verma, V. Pathania, A. Agrawal, V. Chaudhary, A. Manoj Puthur, R. Goyal, A. Pal, T. Danti Dey, A. Kumar, K. Wadhwa, O. Ved

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Background: Skin cancer is now is the buzzing button in the field of medical science. The cyst's pandemic is drastically calibrating the body and well-being of the global village. Methods: The extracted image of the skin tumor cannot be used in one way for diagnosis. The stored image contains anarchies like the center. This approach will locate the forepart of an extracted appearance of skin. Partitioning image models has been presented to sort out the disturbance in the picture. Results: After completing partitioning, feature extraction has been formed by using genetic algorithm and finally, classification can be performed between the trained and test data to evaluate a large scale of an image that helps the doctors for the right prediction. To bring the improvisation of the existing system, we have set our objectives with an analysis. The efficiency of the natural selection process and the enriching histogram is essential in that respect. To reduce the false-positive rate or output, GA is performed with its accuracy. Conclusions: The objective of this task is to bring improvisation of effectiveness. GA is accomplishing its task with perfection to bring down the invalid-positive rate or outcome. The paper's mergeable portion conflicts with the composition of deep learning and medical image processing, which provides superior accuracy. Proportional types of handling create the reusability without any errors.

Keywords: computer-aided system, detection, image segmentation, morphology

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Authors: Sanja O. Podunavac-Kuzmanović, Lidija R. Jevrić, Strahinja Z. Kovačević

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Benzimidazoles are a group of compounds with significant antibacterial, antifungal and anticancer activity. The studied compounds consist of the main benzimidazole structure with different combinations of substituens. This study is based on the two-dimensional and three-dimensional molecular modeling and calculation of molecular descriptors (physicochemical and lipophilicity descriptors) of structurally diverse benzimidazoles. Molecular modeling was carried out by using ChemBio3D Ultra version 14.0 software. The obtained 3D models were subjected to energy minimization using molecular mechanics force field method (MM2). The cutoff for structure optimization was set at a gradient of 0.1 kcal/Åmol. The obtained set of molecular descriptors was used in principal component analysis (PCA) of possible similarities and dissimilarities among the studied derivatives. After the molecular modeling, the quantitative structure-property relationship (QSPR) analysis was applied in order to get the mathematical models which can be used in prediction of pKb values of structurally similar benzimidazoles. The obtained models are based on statistically valid multiple linear regression (MLR) equations. The calculated cross-validation parameters indicate the high prediction ability of the established QSPR models. This study is financially supported by COST action CM1306 and the project No. 114-451-347/2015-02, financially supported by the Provincial Secretariat for Science and Technological Development of Vojvodina.

Keywords: benzimidazoles, chemometrics, molecular modeling, molecular descriptors, QSPR

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Authors: Gangmin Li, Fan Yang

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Personalized recommendation is crucial for any recommendation system. One of the techniques for personalized recommendation is to identify the intention. Traditional user intention identification uses the user’s selection when facing multiple items. This modeling relies primarily on historical behaviour data resulting in challenges such as the cold start, unintended choice, and failure to capture intention when items are new. Motivated by recent advancements in Large Language Models (LLMs) like ChatGPT, we present an approach for user intention identification by embracing LLMs with Chain-of-Thought (CoT) prompting. We use the initial user profile as input to LLMs and design a collection of prompts to align the LLM's response through various recommendation tasks encompassing rating prediction, search and browse history, user clarification, etc. Our tests on real-world datasets demonstrate the improvements in recommendation by explicit user intention identification and, with that intention, merged into a user model.

Keywords: personalized recommendation, generative user modelling, user intention identification, large language models, chain-of-thought prompting

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Authors: Isaac K. E. Ampomah, Seong-Bae Park, Sang-Jo Lee

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Over the past decade, there have been promising developments in Natural Language Processing (NLP) with several investigations of approaches focusing on Recognizing Textual Entailment (RTE). These models include models based on lexical similarities, models based on formal reasoning, and most recently deep neural models. In this paper, we present a sentence encoding model that exploits the sentence-to-sentence relation information for RTE. In terms of sentence modeling, Convolutional neural network (CNN) and recurrent neural networks (RNNs) adopt different approaches. RNNs are known to be well suited for sequence modeling, whilst CNN is suited for the extraction of n-gram features through the filters and can learn ranges of relations via the pooling mechanism. We combine the strength of RNN and CNN as stated above to present a unified model for the RTE task. Our model basically combines relation vectors computed from the phrasal representation of each sentence and final encoded sentence representations. Firstly, we pass each sentence through a convolutional layer to extract a sequence of higher-level phrase representation for each sentence from which the first relation vector is computed. Secondly, the phrasal representation of each sentence from the convolutional layer is fed into a Bidirectional Long Short Term Memory (Bi-LSTM) to obtain the final sentence representations from which a second relation vector is computed. The relations vectors are combined and then used in then used in the same fashion as attention mechanism over the Bi-LSTM outputs to yield the final sentence representations for the classification. Experiment on the Stanford Natural Language Inference (SNLI) corpus suggests that this is a promising technique for RTE.

Keywords: deep neural models, natural language inference, recognizing textual entailment (RTE), sentence-to-sentence relation

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Authors: M. Jerold, V. Sivasubramanian

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In this study, nano zero valent iron Sargassum swartzii (nZVI-SS) biocomposite a marine algal based biosorbent was used for the removal of simulated crystal violet (CV) in batch and continuous fixed bed operation. The Box-Behnen design (BBD) experimental results revealed the biosoprtion was maximum at pH 7.5, biosorbent dosage 0.1 g/L and initial CV concentration of 100 mg/L. The effect of various column parameters like bed depth (3, 6 and 9 cm), flow rate (5, 10 and 15 mL/min) and influent CV concentration (5, 10 and 15 mg/L) were investigated. The exhaustion time increased with increase of bed depth, influent CV concentration and decrease of flow rate. Adam-Bohart, Thomas and Yoon-Nelson models were used to predict the breakthrough curve and to evaluate the model parameters. Out of these models, Thomas and Yoon-Nelson models well described the experimental data. Therefore, the result implies that nZVI-SS biocomposite is a cheap and most promising biosorbent for the removal of CV from wastewater.

Keywords: algae, biosorption, zero-valent, dye, wastewater

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Authors: Shohreh Ghasemi

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Introduction: Melanoma is a complex disease with various clinical and histopathological features that impact prognosis and treatment decisions. Traditional methods of melanoma prognosis involve manual examination and interpretation of clinical and histopathological data by dermatologists and pathologists. However, the subjective nature of these assessments can lead to inter-observer variability and suboptimal prognostic accuracy. AI, with its ability to analyze vast amounts of data and identify patterns, has emerged as a promising tool for improving melanoma prognosis. Methods: A comprehensive literature search was conducted to identify studies that employed AI techniques for melanoma prognosis. The search included databases such as PubMed and Google Scholar, using keywords such as "artificial intelligence," "melanoma," and "prognosis." Studies published between 2010 and 2022 were considered. The selected articles were critically reviewed, and relevant information was extracted. Results: The review identified various AI methodologies utilized in melanoma prognosis, including machine learning algorithms, deep learning techniques, and computer vision. These techniques have been applied to diverse data sources, such as clinical images, dermoscopy images, histopathological slides, and genetic data. Studies have demonstrated the potential of AI in accurately predicting melanoma prognosis, including survival outcomes, recurrence risk, and response to therapy. AI-based prognostic models have shown comparable or even superior performance compared to traditional methods.

Keywords: artificial intelligence, melanoma, accuracy, prognosis prediction, image analysis, personalized medicine

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Authors: Minna-Maari Harmaala, Hanna Harilainen

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Estimates of the economic value of transitioning to circular economy models vary but it has been estimated to represent $1 trillion worth of new business into the global economy. In Europe alone, estimates claim that adopting circular-economy principles could not only have environmental and social benefits but also generate a net economic benefit of €1.8 trillion by 2030. Proponents of a circular economy argue that it offers a major opportunity to increase resource productivity, decrease resource dependence and waste, and increase employment and growth. A circular system could improve competitiveness and unleash innovation. Yet, most companies are not capturing these opportunities and thus the even abundant circular opportunities remain uncaptured even though they would seem inherently profitable. Service design in broad terms relates to developing an existing or a new service or service concept with emphasis and focus on the customer experience from the onset of the development process. Service design may even mean starting from scratch and co-creating the service concept entirely with the help of customer involvement. Service design methodologies provide a structured way of incorporating customer understanding and involvement in the process of designing better services with better resonance to customer needs. A business model is a depiction of how the company creates, delivers, and captures value; i.e. how it organizes its business. The process of business model development and adjustment or modification is also called business model innovation. Innovating business models has become a part of business strategy. Our hypothesis is that in addition to linear models still being easier to adopt and often with lower threshold costs, companies lack an understanding of how circular models can be adopted into their business and how customers will be willing and ready to adopt the new circular business models. In our research, we use robust service design methodology to develop circular economy solutions with two case study companies. The aim of the process is to not only develop the service concepts and portfolio, but to demonstrate the willingness to adopt circular solutions exists in the customer base. In addition to service design, we employ business model innovation methods to develop, test, and validate the new circular business models further. The results clearly indicate that amongst the customer groups there are specific customer personas that are willing to adopt and in fact are expecting the companies to take a leading role in the transition towards a circular economy. At the same time, there is a group of indifferents, to whom the idea of circularity provides no added value. In addition, the case studies clearly show what changes adoption of circular economy principles brings to the existing business model and how they can be integrated.

Keywords: business model innovation, circular economy, circular economy business models, service design

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Authors: Elhameh Narimani, Mikhail Sorin, Philippe Micheau, Hakim Nesreddine

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Numerical models of the heat exchangers in ejector refrigeration system (ERS) were developed and validated with the experimental data. The models were based on the switched heat exchangers model using the moving boundary method, which were capable of estimating the zones’ lengths, the outlet temperatures of both sides and the heat loads at various experimental points. The developed models were utilized to investigate the influence of the primary flow pressure on the performance of an R245fa ERS based on its coefficient of performance (COP) and exergy efficiency. It was illustrated numerically and proved experimentally that increasing the primary flow pressure slightly reduces the COP while the exergy efficiency goes through a maximum before decreasing.

Keywords: Coefficient of Performance, COP, Ejector Refrigeration System, ERS, exergy efficiency (ηII), heat exchangers modeling, moving boundary method

Procedia PDF Downloads 202