Search results for: sustainable tourism models

Authors: Tsega Asresa, Getahun Tigistu, Melaku Bayih

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Computer vision is a subfield of artificial intelligence that allows computers and systems to extract meaning from digital images and video. It is used in a wide range of fields of study, including self-driving cars, video surveillance, medical diagnosis, manufacturing, law, agriculture, quality control, health care, facial recognition, and military applications. Aromatic medicinal plants are botanical raw materials used in cosmetics, medicines, health foods, essential oils, decoration, cleaning, and other natural health products for therapeutic and Aromatic culinary purposes. These plants and their products not only serve as a valuable source of income for farmers and entrepreneurs but also going to export for valuable foreign currency exchange. In Ethiopia, there is a lack of technologies for the classification and identification of Aromatic medicinal plant parts and disease type cured by aromatic medicinal plants. Farmers, industry personnel, academicians, and pharmacists find it difficult to identify plant parts and disease types cured by plants before ingredient extraction in the laboratory. Manual plant identification is a time-consuming, labor-intensive, and lengthy process. To alleviate these challenges, few studies have been conducted in the area to address these issues. One way to overcome these problems is to develop a deep learning model for efficient identification of Aromatic medicinal plant parts with their corresponding disease type. The objective of the proposed study is to identify the aromatic medicinal plant parts and their disease type classification using computer vision technology. Therefore, this research initiated a model for the classification of aromatic medicinal plant parts and their disease type by exploring computer vision technology. Morphological characteristics are still the most important tools for the identification of plants. Leaves are the most widely used parts of plants besides roots, flowers, fruits, and latex. For this study, the researcher used RGB leaf images with a size of 128x128 x3. In this study, the researchers trained five cutting-edge models: convolutional neural network, Inception V3, Residual Neural Network, Mobile Network, and Visual Geometry Group. Those models were chosen after a comprehensive review of the best-performing models. The 80/20 percentage split is used to evaluate the model, and classification metrics are used to compare models. The pre-trained Inception V3 model outperforms well, with training and validation accuracy of 99.8% and 98.7%, respectively.

Keywords: aromatic medicinal plant, computer vision, convolutional neural network, deep learning, plant classification, residual neural network

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Authors: Jefferson Hernandez, Juan Padilla

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Estimation of price elasticity of demand is a valuable tool for the task of price settling. Given its relevance, it is an active field for microeconomic and statistical research. Price elasticity in the industry of oil and gas, in particular for fuels sold in gas stations, has shown to be a challenging topic given the market and state restrictions, and underlying correlations structures between the types of fuels sold by the same gas station. This paper explores the Lotka-Volterra model for the problem for price elasticity estimation in the context of fuels; in addition, it is introduced multivariate random effects with the purpose of dealing with errors, e.g., measurement or missing data errors. In order to model the underlying correlation structures, the Inverse-Wishart, Hierarchical Half-t and LKJ distributions are studied. Here, the Bayesian paradigm through Markov Chain Monte Carlo (MCMC) algorithms for model estimation is considered. Simulation studies covering a wide range of situations were performed in order to evaluate parameter recovery for the proposed models and algorithms. Results revealed that the proposed algorithms recovered quite well all model parameters. Also, a real data set analysis was performed in order to illustrate the proposed approach.

Keywords: price elasticity, volume, correlation structures, Bayesian models

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Authors: Luis Fonseca, Filipe Cabral Pinto, Susana Sargento

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The increase of the world population, especially in large urban centers, has resulted in new challenges particularly with the control and optimization of public safety. Thus, in the present work, a solution is proposed for the prediction of criminal occurrences in a city based on historical data of incidents and demographic information. The entire research and implementation will be presented start with the data collection from its original source, the treatment and transformations applied to them, choice and the evaluation and implementation of the Machine Learning model up to the application layer. Classification models will be implemented to predict criminal risk for a given time interval and location. Machine Learning algorithms such as Random Forest, Neural Networks, K-Nearest Neighbors and Logistic Regression will be used to predict occurrences, and their performance will be compared according to the data processing and transformation used. The results show that the use of Machine Learning techniques helps to anticipate criminal occurrences, which contributed to the reinforcement of public security. Finally, the models were implemented on a platform that will provide an API to enable other entities to make requests for predictions in real-time. An application will also be presented where it is possible to show criminal predictions visually.

Keywords: crime prediction, machine learning, public safety, smart city

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Authors: Aljon Pangan

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The Philippines being an archipelagic country, is surrounded by coastlines (36,289 km), coastal waters (226,000 km²), oceanic waters (1.93 million km²) and territorial waters (2.2 million km²). Studies show that the Philippine coastal ecosystems are the most productive and biologically diverse in the world, however, plagued by degradation problems due to over-exploitation and illegal activities. The existence of coastal degradation issues in the country led to the emergence of Coastal Resource Management (CRM) as an approach to both national and local government in providing solutions for sustainable coastal resource utilization. CRM applies the idea of planning, implementing and monitoring through the lens of collaborative governance. It utilizes collective action and decision-making to achieve sustainable use of coastal resources. The Municipality of Bagac in Bataan is one of the coastal municipalities in the country who crafts its own CRM Program as a solution to coastal resource degradation and problems. Information and Communications Technology (ICT), particularly Integrated Imaging Management System (IIMS) is one approach that can be applied in the formula of collaborative governance which entails the Government, Private Sector, and Civil Society. IIMS can help policymakers, managers, and citizens in managing coastal resources through analyzed spatial data describing the physical, biological, and socioeconomic characteristics of the coastal areas. Moreover, this study will apply the qualitative approach in deciphering possible impacts of the application of IIMS in the Coastal Resource Management policy making and implementation of the Municipality of Bagac.

Keywords: coastal resource management, collaborative governance, integrated imaging management system, information and communication technology

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Authors: Erick Pruchnicki, Nikhil Padhye

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Plates are important engineering structures which have attracted extensive research since the 19th century. The subject of this work is statical analysis of a linearly elastic homogenous plate under small deformations. A 'thin plate' is a three-dimensional structure comprising of a small transverse dimension with respect to a flat mid-surface. The general aim of any plate theory is to deduce a two-dimensional model, in terms of mid-surface quantities, to approximately and accurately describe the plate's deformation in terms of mid-surface quantities. In recent decades, a common starting point for this purpose is to utilize series expansion of a displacement field across the thickness dimension in terms of the thickness parameter (h). These attempts are mathematically consistent in deriving leading-order plate theories based on certain a priori scaling between the thickness and the applied loads; for example, asymptotic methods which are aimed at generating leading-order two-dimensional variational problems by postulating formal asymptotic expansion of the displacement fields. Such methods rigorously generate a hierarchy of two-dimensional models depending on the order of magnitude of the applied load with respect to the plate-thickness. However, in practice, applied loads are external and thus not directly linked or dependent on the geometry/thickness of the plate; thus, rendering any such model (based on a priori scaling) of limited practical utility. In other words, the main limitation of these approaches is that they do not furnish a single plate model for all orders of applied loads. Following analogy of recent efforts of deploying Fourier-series expansion to study convergence of reduced models, we propose two-dimensional model(s) resulting from truncation of the potential energy and rigorously prove the convergence of these two-dimensional plate models to the parent three-dimensional linear elasticity with increasing truncation order of the potential energy.

Keywords: plate theory, Fourier-series expansion, convergence result, Legendre polynomials

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Authors: Geisa Lima Mesquita Zambrosi, Maisa Ciampi Guillardi, Flávia Rodrigues Patrício, Oliveiro Guerreiro Filho

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Certified agricultural products are important components of the food industry. However, certifiers have been expanding the list of restricted or prohibited pesticides, limiting the options of products for phytosanitary control of plant diseases, but without offering alternatives to the farmers. Soybean and coffee leaf rust, brown eye spots, and Phoma leaf spots are the main fungal diseases that pose a serious threat to soybean and coffee cultivation worldwide. In conventional farming systems, these diseases are controlled by using synthetic fungicides, which, in addition to intensifying the occurrence of fungal resistance, are highly toxic to the environment, farmers, and consumers. In organic, agroecological, or regenerative farming systems, product options for plant protection are limited, being available only copper-based compounds, and biodefensivesornon-standard homemade products. Therefore, there is a growing demand for effective bioprotectors with low environmental impact for adoption in more sustainable agricultural systems. Then, to contribute to covering such a gap, we have developed a compound based on plant extracts and metallic elements for foliar application. This product has both biostimulant and bioprotective action, which promotes sustainable disease control, increases productivity as well as reduces damage to the environment. The product's components have complementary mechanisms that promote protection against the disease by directly acting on the pathogens and activating the plant's natural defense system. The protective ability of the product against three coffee diseases (coffee leaf rust, brown eye spot, and Phoma leaf spot) and against soybean rust disease was evaluated, in addition to its ability to promote plant growth. Our goal is to offer an effective alternative to control the main coffee fungal diseases and soybean fungal diseases, with a biostimulant effect and low toxicity. The proposed product can also be part of the integrated management of coffee and soybean diseases in conventional farming associated with chemical and biological pesticides, offering the market a sustainable coffee and soybean with high added value and low residue content. Experiments were carried out under controlled conditions to evaluate the effectiveness of the product in controlling rust, phoma, and cercosporiosis in comparison to control-inoculated plants that did not receive the product. The in vitro and in vivo effects of the product on the pathogen were evaluated using light microscopy and scanning electron microscopy, respectively. The fungistatic action of the product was demonstrated by a reduction of 85% and 95% in spore germination and disease symptoms severity on the leaves of coffee plants, respectively. The formulation had both a protective effect, acting to prevent infection by coffee leaf rust, and a curative effect, reducing the rust symptoms after its establishment.

Keywords: plant disease, natural fungicide, plant health, sustainability, alternative disease management

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Authors: Etienne Mfoumou, Mo Shamma, Martin Tango, Michael Locke

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Water literacy is essential for addressing emerging water management challenges in southwest Nova Scotia (SWNS), where growing concerns over water scarcity and sustainability have highlighted the need for improved educational frameworks. Current approaches often fail to fully represent the complexity of water systems, focusing narrowly on the water cycle while neglecting critical aspects such as groundwater infiltration and the interconnectedness of surface and subsurface water systems. To address these gaps, this paper proposes a comprehensive framework for water literacy that integrates the physical dimensions of water systems with key aspects of understanding, including processes, energy, scale, and human dependency. Moreover, a suggested tool to enhance this framework is a real-time hydrometric data map supported by a network of water level monitoring devices deployed across the province. These devices, particularly for monitoring dug wells, would provide critical data on groundwater levels and trends, offering stakeholders actionable insights into water availability and sustainability. This real-time data would facilitate deeper understanding and engagement with local water issues, complementing the educational framework and empowering stakeholders to make informed decisions. By integrating this tool, the proposed framework offers a practical, interdisciplinary approach to improving water literacy and promoting sustainable water management in SWNS.

Keywords: water education, water literacy, water management, water systems, Southwest Nova Scotia

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Authors: Jonathan Josephs-Spaulding, Hannah Rettig, Simon Graspeunter, Jan Rupp, Christoph Kaleta

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Background: Recurrent urinary tract infections (rUTIs) are a global cause of emergency room visits and represent a significant burden for public health systems. Therefore, metatranscriptomic approaches to investigate metabolic exchange and crosstalk between uropathogenic Escherichia coli (UPEC), which is responsible for 90% of UTIs, and collaborating pathogens of the urogenital microbiome is necessary to better understand the pathogenetic processes underlying rUTIs. Objectives: This study aims to determine the level in which uropathogens optimize the host urinary metabolic environment to succeed during invasion. By developing patient-specific metabolic models of infection, these observations can be taken advantage of for the precision treatment of human disease. Methods: To date, we have set up an rUTI patient cohort and observed various urine-associated pathogens. From this cohort, we developed patient-specific metabolic models to predict bladder microbiome metabolism during rUTIs. This was done by creating an in silico metabolomic urine environment, which is representative of human urine. Metabolic models of uptake and cross-feeding of rUTI pathogens were created from genomes in relation to the artificial urine environment. Finally, microbial interactions were constrained by metatranscriptomics to indicate patient-specific metabolic requirements of pathogenic communities. Results: Metabolite uptake and cross-feeding are essential for strain growth; therefore, we plan to design patient-specific treatments by adjusting urinary metabolites through nutritional regimens to counteract uropathogens by depleting essential growth metabolites. These methods will provide mechanistic insights into the metabolic components of rUTI pathogenesis to provide an evidence-based tool for infection treatment.

Keywords: recurrent urinary tract infections, human microbiome, uropathogenic Escherichia coli, UPEC, microbial ecology

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Authors: Thi Phuong Pham

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This paper explores the intricate relationships between Sustainable Supply Chain Management (SSCM) practices, digital transformation (DT), and enterprise performance within the context of Vietnam. Over the past two decades, there has been a paradigm shift in supply chain management, with sustainability gaining prominence due to increasing concerns about climate change, labor practices, and the environmental impact of business operations. In the ever-evolving realm of global business, sustainability and digital transformation (DT) intersecting dynamics have become pivotal catalysts for organizational success. This research investigates how integrating SSCM with DT can enhance enterprise performance, a subject of significant relevance as Vietnam undergoes rapid economic growth and digital transformation. The primary objectives of this research are twofold: (1) to examine the effects of SSCM practices on enterprise performance in three critical aspects: economic, environmental, and social performance in Vietnam and (2) to explore the mediating role of DT in this relationship. By analyzing these dynamics, the study aims to provide valuable insights for policymakers and the academic community regarding the potential benefits of aligning SSCM principles with digital technologies. To achieve these objectives, the research employs a robust mixed-method approach. The research begins with a comprehensive literature review to establish a theoretical framework that underpins the empirical analysis. Data collection was conducted through a structured survey targeting Vietnamese enterprises, with the survey instrument designed to measure SSCM practices, DT, and enterprise performance using a five-point Likert scale. The reliability and validity of the survey were ensured by pre-testing with industry practitioners and refining the questionnaire based on their feedback. For data analysis, structural equation modeling (SEM) was employed to quantify the direct effects of SSCM on enterprise performance, while mediation analysis using the PROCESS Macro 4.0 in SPSS was conducted to assess the mediating role of DT. The findings reveal that SSCM practices positively influence enterprise performance by enhancing operational efficiency, reducing costs, and improving sustainability metrics. Furthermore, DT acts as a significant mediator, amplifying the positive impacts of SSCM practices through improved data management, enhanced communication, and more agile supply chain processes. These results underscore the critical role of DT in maximizing the benefits of SSCM practices, particularly in a developing economy like Vietnam. This research contributes to the existing body of knowledge by highlighting the synergistic effects of SSCM and DT on enterprise performance. It offers practical implications for businesses that enhance their sustainability and digital capabilities, providing a roadmap for integrating these two pivotal aspects to achieve competitive advantage. The study's insights can also inform governmental policies designed to foster sustainable economic growth and digital innovation in Vietnam.

Keywords: sustainable supply chain management, digital transformation, enterprise performance, Vietnam

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Authors: Nikola Tomasevic, Marko Batic, Sanja Vranes

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Emerging Semantic Web technologies can be seen as the next step in evolution of the intelligent facility management systems. Particularly, this considers increased usage of open source and/or standardized concepts for data classification and semantic interpretation. To deliver such facility management systems, providing the comprehensive integration and interoperability platform in from of the facility data model is a prerequisite. In this paper, one of the possible modelling approaches to provide such integrative facility data model which was based on the ontology modelling concept was presented. Complete ontology development process, starting from the input data acquisition, ontology concepts definition and finally ontology concepts population, was described. At the beginning, the core facility ontology was developed representing the generic facility infrastructure comprised of the common facility concepts relevant from the facility management perspective. To develop the data model of a specific facility infrastructure, first extension and then population of the core facility ontology was performed. For the development of the full-blown facility data models, Malpensa and Fiumicino airports in Italy, two major European air-traffic hubs, were chosen as a test-bed platform. Furthermore, the way how these ontology models supported the integration and interoperability of the overall airport energy management system was analyzed as well.

Keywords: airport ontology, energy management, facility data model, ontology modeling

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Authors: Mojtaba Taheri, Saied Reza Ameli

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In this research, we have calculated the effect of membership in international climate change treaties for 20 developed countries based on the human development index (HDI) and compared this effect with the process of pollutant reduction in the Environmental Kuznets Curve (EKC) theory. For this purpose, the data related to The real GDP per capita with 2010 constant prices is selected from the World Development Indicators (WDI) database. Ecological Footprint (ECOFP) is the amount of biologically productive land needed to meet human needs and absorb carbon dioxide emissions. It is measured in global hectares (gha), and the data retrieved from the Global Ecological Footprint (2021) database will be used, and we will proceed by examining step by step and performing several series of targeted statistical regressions. We will examine the effects of different control variables, including Energy Consumption Structure (ECS) will be counted as the share of fossil fuel consumption in total energy consumption and will be extracted from The United States Energy Information Administration (EIA) (2021) database. Energy Production (EP) refers to the total production of primary energy by all energy-producing enterprises in one country at a specific time. It is a comprehensive indicator that shows the capacity of energy production in the country, and the data for its 2021 version, like the Energy Consumption Structure, is obtained from (EIA). Financial development (FND) is defined as the ratio of private credit to GDP, and to some extent based on the stock market value, also as a ratio to GDP, and is taken from the (WDI) 2021 version. Trade Openness (TRD) is the sum of exports and imports of goods and services measured as a share of GDP, and we use the (WDI) data (2021) version. Urbanization (URB) is defined as the share of the urban population in the total population, and for this data, we used the (WDI) data source (2021) version. The descriptive statistics of all the investigated variables are presented in the results section. Related to the theories of sustainable development, Environmental Kuznets Curve (EKC) is more significant in the period of study. In this research, we use more than fourteen targeted statistical regressions to purify the net effects of each of the approaches and examine the results.

Keywords: climate change, globalization, environmental economics, sustainable development, international climate treaty

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Authors: Antonella Cartoni, Mattea Carmen Castrovilli

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A biosensor for lactate detection has been developed using an environmentally friendly approach. The biosensor is based on lactate oxidase (LOX) and has remarkable capabilities for reuse and storage at room temperature. The manufacturing technique employed is ambient electrospray deposition (ESD), which enables efficient and sustainable immobilization of the LOX enzyme on a cost-effective com-mercial screen-printed Prussian blue/carbon electrode (PB/C-SPE). The study demonstrates that the ESD technology allows the biosensor to be stored at ambient pressure and temperature for extended periods without affecting the enzymatic activity. The biosensor can be stored for up to 90 days without requiring specific storage conditions, and it can be reused for up to 24 measurements on both freshly prepared electrodes and electrodes that are three months old. The LOX-based biosensor exhibits a lin-ear range of lactate detection between 0.1 and 1 mM, with a limit of detection of 0.07±0.02 mM. Ad-ditionally, it does not exhibit any memory effects. The immobilization process does not involve the use of entrapment matrices or hazardous chemicals, making it environmentally sustainable and non-toxic compared to current methods. Furthermore, the application of a electrospray deposition cycle on previously used biosensors rejuvenates their performance, making them comparable to freshly made biosensors. This highlights the excellent recycling potential of the technique, eliminating the waste as-sociated with disposable devices.

Keywords: green friendly, reuse, storage performance, immobilization, matrix-free, electrospray deposition, biosensor, lactate oxidase, enzyme

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Authors: Chokri Slim

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The objective of this research is attempting to respond to this question: Is there a significant difference between the regression model and statistical learning models in predicting credit risk management? A Multiple Linear Regression (MLR) model was compared with neural networks including Multi-Layer Perceptron (MLP), and a Support vector regression (SVR). The population of this study includes 50 listed Banks in Tunis Stock Exchange (TSE) market from 2000 to 2016. Firstly, we show the factors that have significant effect on the quality of loan portfolios of banks in Tunisia. Secondly, it attempts to establish that the systematic use of objective techniques and methods designed to apprehend and assess risk when considering applications for granting credit, has a positive effect on the quality of loan portfolios of banks and their future collectability. Finally, we will try to show that the bank governance has an impact on the choice of methods and techniques for analyzing and measuring the risks inherent in the banking business, including the risk of non-repayment. The results of empirical tests confirm our claims.

Keywords: credit risk management, multiple linear regression, principal components analysis, artificial neural networks, support vector machines

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Authors: Abdessalam Hijab, Hafida Boulekbache, Eric Henry

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According to several research studies, the city as a whole is a complex, spatially organized system; its modeling must take into account several factors, socio-economic, and political, or geographical, acting at multiple scales of observation according to varied temporalities. Sustainable management and protection of the environment in this complex system require significant human and technical investment, particularly for monitoring and maintenance. The objective of this paper is to propose an intelligent approach based on the coupling of Geographic Information System (GIS) and Information and Communications Technology (ICT) tools in order to integrate the inhabitants in the processes of sustainable management and protection of the urban environment, specifically in the processes of household waste collection in urban areas. We are discussing a collaborative 'city/inhabitant' space. Indeed, it is a geo-collaborative approach, based on the spatialization and real-time geo-localization of topological and multimedia data taken by the 'active' inhabitant, in the form of geo-localized alerts related to household waste issues in their city. Our proposal provides a good understanding of the extent to which civil society (inhabitants) can help and contribute to the development of complementary solutions for the collection of household waste and the protection of the urban environment. Moreover, it allows the inhabitant to contribute to the enrichment of a data bank for future uses. Our geo-collaborative model will be tested in the Lamkansa sampling district of the city of Casablanca in Morocco.

Keywords: geographic information system, GIS, information and communications technology, ICT, geo-collaboration, inhabitants, city

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Authors: Stuti Modi, Aditi Kambli

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This paper addresses the need for intelligent water management and distribution system in smart cities to ensure optimal consumption and distribution of water for drinking and sanitation purposes. Water being a limited resource in cities require an effective system for collection, storage and distribution. In this paper, applications of two mostly widely used particular types of data-driven models, namely artificial neural networks (ANN) and fuzzy logic-based models, to modelling in the water resources management field are considered. The objective of this paper is to review the principles of various types and architectures of neural network and fuzzy adaptive systems and their applications to integrated water resources management. Final goal of the review is to expose and formulate progressive direction of their applicability and further research of the AI-related and data-driven techniques application and to demonstrate applicability of the neural networks, fuzzy systems and other machine learning techniques in the practical issues of the regional water management. Apart from this the paper will deal with water storage, using ANN to find optimum reservoir level and predicting peak daily demands.

Keywords: artificial neural networks, fuzzy systems, peak daily demand prediction, water management and distribution

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Authors: Seyedomid Fatemi

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The walls of the building, as the main intermediary between the outside and the inside of the building, play an important role in controlling the environmental conditions and ensuring the comfort of the residents, thus reducing the heating and cooling loads. Therefore, the use of suitable materials is considered one of the simplest and most effective ways to reduce the heating and cooling loads of the building, which will also save energy. Therefore, in order to achieve the goal of the research "Ranking of optimal materials for building walls," optimal materials for building walls in a temperate and humid climate (case example: Sari city) from the perspective of embodied energy, waste of electricity and gas energy, cost and reuse been investigated to achieve sustainable architecture. In this regard, using information obtained from Sari Municipality, design components have been presented by experts using the Delphi method. Considering the criteria of experts' opinions (cost and reuse), the amount of embodied energy of the materials, as well as the amount of waste of electricity and gas of different materials of the walls, with the help of the AHP weighting technique and finally with the TOPSIS technique, the best type of materials in the order of 1- 3-D Panel 2-ICF-, 3-Cement block with pumice, 4-Wallcrete block, 5-Clay block, 6-Autoclaved Aerated Concrete (AAC), 7-Foam cement block, 8-Aquapanel and 9-Reinforced concrete wall for use in The walls of the buildings were proposed in Sari city.

Keywords: optimum materials, building walls, moderate and humid climate, sustainable architecture, AHP-TOPSIS technique

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Authors: K. Veluraja

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Glycan of glycolipids and glycoproteins is playing a significant role in living systems particularly in molecular recognition processes. Molecular recognition processes are attributed to their occurrence on the surface of the cell, sequential arrangement and type of sugar molecules present in the oligosaccharide structure and glyosidic linkage diversity (glycoinformatics) and conformational diversity (glycoconformatics). Molecular Dynamics Simulation study is a theoretical-cum-computational tool successfully utilized to establish glycoconformatics of glycan. The study on various oligosaccharides of glycan clearly indicates that oligosaccharides do exist in multiple conformational states and these conformational states arise due to the flexibility associated with a glycosidic torsional angle (φ,ψ) . As an example: a single disaccharide structure NeuNacα(2-3) Gal exists in three different conformational states due to the differences in the preferential value of glycosidic torsional angles (φ,ψ). Hence establishing three dimensional structural and conformational models for glycan (cartesian coordinates of every individual atoms of an oligosaccharide structure in a preferred conformation) is quite crucial to understand various molecular recognition processes such as glycan-toxin interaction and glycan-virus interaction. The gycoconformatics models obtained for various glycan through Molecular Dynamics Simulation stored in our 3DSDSCAR (3DSDSCAR.ORG) a public domain database and its utility value in understanding the molecular recognition processes and in drug design venture will be discussed.

Keywords: glycan, glycoconformatics, molecular dynamics simulation, oligosaccharide

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Authors: Irene Yi

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Machine learning, natural language processing, and neural network models of language are becoming more and more prevalent in the fields of technology and linguistics today. Training data for machines are at best, large corpora of human literature and at worst, a reflection of the ugliness in society. Computational linguistics is a growing field dealing with such issues of data collection for technological development. Machines have been trained on millions of human books, only to find that in the course of human history, derogatory and sexist adjectives are used significantly more frequently when describing females in history and literature than when describing males. This is extremely problematic, both as training data, and as the outcome of natural language processing. As machines start to handle more responsibilities, it is crucial to ensure that they do not take with them historical sexist and misogynistic notions. This paper gathers data and algorithms from neural network models of language having to deal with syntax, semantics, sociolinguistics, and text classification. Computational analysis on such linguistic data is used to find patterns of misogyny. Results are significant in showing the existing intentional and unintentional misogynistic notions used to train machines, as well as in developing better technologies that take into account the semantics and syntax of text to be more mindful and reflect gender equality. Further, this paper deals with the idea of non-binary gender pronouns and how machines can process these pronouns correctly, given its semantic and syntactic context. This paper also delves into the implications of gendered grammar and its effect, cross-linguistically, on natural language processing. Languages such as French or Spanish not only have rigid gendered grammar rules, but also historically patriarchal societies. The progression of society comes hand in hand with not only its language, but how machines process those natural languages. These ideas are all extremely vital to the development of natural language models in technology, and they must be taken into account immediately.

Keywords: computational analysis, gendered grammar, misogynistic language, neural networks

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Authors: Ammara Mehmood, Aneela Zameer, Muhammad Asif Zahoor Raja, Muhammad Faisal Fateh

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In this work, the strength of bio-inspired computational intelligence based technique is exploited for parameter estimation for the periodic signals using Continuous Differential Evolution (CDE) by defining an error function in the mean square sense. Multidimensional and nonlinear nature of the problem emerging in sinusoidal signal models along with noise makes it a challenging optimization task, which is dealt with robustness and effectiveness of CDE to ensure convergence and avoid trapping in local minima. In the proposed scheme of Continuous Differential Evolution based Signal Parameter Estimation (CDESPE), unknown adjustable weights of the signal system identification model are optimized utilizing CDE algorithm. The performance of CDESPE model is validated through statistics based various performance indices on a sufficiently large number of runs in terms of estimation error, mean squared error and Thiel’s inequality coefficient. Efficacy of CDESPE is examined by comparison with the actual parameters of the system, Genetic Algorithm based outcomes and from various deterministic approaches at different signal-to-noise ratio (SNR) levels.

Keywords: parameter estimation, bio-inspired computing, continuous differential evolution (CDE), periodic signals

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Authors: David Wuepper, Marc Patry

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UNESCO’s World Heritage program requests the inscribed locations to promote the World Heritage brand by clearly presenting information about it on-site. Based on feedback from over 319,000 visitors at 791 locations, we create an index that shows how much the World Heritage sites actually brand themselves as such. We find great heterogeneity throughout the list and explain this econometrically mostly with the economic benefit for the sites but also with cultural brand preferences, which are highest in Asia, followed by Europe and North America. We also find a positive relationship between World Heritage branding and conservation status and a U-shaped relationship between visitor numbers and WH branding. Based on our findings, we recommend to make clear World Heritage branding mandatory for all sites.

Keywords: UNESCO World Heritage, collective brand, cultural tourism, heritage conservation, brand equity, spatial econometrics

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Authors: Mohammad Bakhshi, Firas Al Janabi

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High resolution rain data are very important to fulfill the input of hydrological models. Among models of high-resolution rainfall data generation, the temporal disaggregation was chosen for this study. The paper attempts to generate three different rainfall resolutions (4-hourly, hourly and 10-minutes) from daily for around 20-year record period. The process was done by DiMoN tool which is based on random cascade model and method of fragment. Differences between observed and simulated rain dataset are evaluated with variety of statistical and empirical methods: Kolmogorov-Smirnov test (K-S), usual statistics, and Exceedance probability. The tool worked well at preserving the daily rainfall values in wet days, however, the generated data are cumulated in a shorter time period and made stronger storms. It is demonstrated that the difference between generated and observed cumulative distribution function curve of 4-hourly datasets is passed the K-S test criteria while in hourly and 10-minutes datasets the P-value should be employed to prove that their differences were reasonable. The results are encouraging considering the overestimation of generated high-resolution rainfall data.

Keywords: DiMoN Tool, disaggregation, exceedance probability, Kolmogorov-Smirnov test, rainfall

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Authors: Komala Arsi, Ann M. Donoghue, Dan J. Donoghue

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Methionine is an essential dietary amino acid necessary to promote growth and health of poultry. Synthetic methionine is commonly used as a supplement in conventional poultry diets and is temporarily allowed in organic poultry feed for lack of natural and organically approved sources of methionine. It has been a challenge to find a natural, sustainable and cost-effective source for methionine which reiterates the pressing need to explore potential alternatives of methionine for organic poultry production. Fish have high concentrations of methionine, but wild-caught fish are expensive and adversely impact wild fish populations. Asian carp (AC) is an invasive species and its utilization has the potential to be used as a natural methionine source. However, to our best knowledge, there is no proven technology to utilize this fish as a methionine source. In this study, we co-extruded Asian carp and soybean meal to form a dry-extruded, methionine-rich AC meal. In order to formulate rations with the novel extruded carp meal, the product was tested on cecectomized roosters for its amino acid digestibility and total metabolizable energy (TMEn). Excreta was collected and the gross energy, protein content of the feces was determined to calculate Total Metabolizable Energy (TME). The methionine content, digestibility and TME values were greater for the extruded AC meal than control diets. Carp meal was subsequently tested as a methionine source in feeds formulated for broilers, and production performance (body weight gain and feed conversion ratio) was assessed in comparison with broilers fed standard commercial diets supplemented with synthetic methionine. In this study, broiler chickens were fed either a control diet with synthetic methionine or a treatment diet with extruded AC meal (8 replicates/treatment; n=30 birds/replicate) from day 1 to 42 days of age. At the end of the trial, data for body weights, feed intake and feed conversion ratio (FCR) was analyzed using one-way ANOVA with Fisher LSD test for multiple comparisons. Results revealed that birds on AC diet had body weight gains and feed intake comparable to diets containing synthetic methionine (P > 0.05). Results from the study suggest that invasive AC-derived fish meal could potentially be an effective and inexpensive source of sustainable natural methionine for organic poultry farmers.

Keywords: Asian carp, methionine, organic, poultry

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Authors: Constâncio Augusto Machanguana, Luís Miguel Estevão Cristóvão

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In Mozambique, over 80% of the rural population relies on agriculture, livestock, and silviculture for their livelihoods. Unfortunately, these communities face persistent food shortages, which are exacerbated by natural disasters and post-harvest losses due to inadequate storage facilities. Addressing post-harvest loss is critical not only for ensuring food security but also for preventing financial hardships faced by farmers. The study delves into the perceptions of beneficiary communities regarding the construction of three food dryer models made from metal, wood, and clay brick. These solar dryers are part of the project titled ‘Solar Dryer Integrated with Natural Rocks as Energy Storage for Drying Fruits and Vegetables in Mozambique.’ The overarching goal is to enhance food availability beyond the typical growing season, particularly for fruits and vegetables, while simultaneously combating hunger. Given the context of climate change impacts on agriculture, this project becomes even more relevant. Structured interviews conducted with 45 members of beneficiary associations in Manica Province—primarily female heads of households—revealed that rural communities are aware of various food drying alternatives. However, reliance on traditional methods often comes at a cost: compromised product quality and reduced shelf life. To address these challenges, the project implemented energy storage solutions like rock-based thermal energy storage for food drying. This result underscores the urgent need to foster innovation and extend these sustainable practices —such as solar dryers integrated with thermal energy-storage systems made of locally abundant and affordable materials— to more local communities, especially those with significant agricultural potential within the country. By taking these actions, we can improve food security and alleviate hunger.

Keywords: solar dryer, food security, rural community, small technology

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Authors: Areej Al-Hamad, Cheryl Forchuk, Abe Oudshoorn, Gerald Patrick Mckinley

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Syrian refugee women face many obstacles when accessing health services in host countries that are influenced by various cultural, structural, and practical factors. This paper is based on critical ethnographic research undertaken in Canada to explore Syrian refugee women's migration experiences. Also, we aim to critically examine how the intersection of gender, trauma, violence and the political and economic conditions of Syrian refugee women shapes their everyday lives and health. The study also investigates the strategies and practices by which Syrian refugee women are currently addressing their healthcare needs and the models of care that are suggested for meeting their physical and mental health needs. Findings show that these women experienced constant worries, hardship, vulnerability, and intrusion of dignity. These experiences and challenges were aggravated by the structure of the Canadian social and health care system. This study offers a better understanding of the impact of migration and trauma on Syrian refugee women's roles, responsibilities, gender dynamics, and interaction with Ontario's healthcare system to improve interaction and outcomes. Health care models should address these challenges among Syrian refugee families in Canada.

Keywords: Syrian refugee women, intersectionality, critical ethnography, migration

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Authors: Farida, M. Shehu, Shehu U. R. Aliyu

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Access to finance has been recognized in the literature as one of the major impediments confronting small scale businesses (SSBs). This largely arises due to high lending rate, religious inclinations, collateral, etc. Islamic mode finance operates under Profit and Loss Sharing (PLS) arrangement between a borrower (business owner) and a lender (Islamic bank). This paper empirically assesses the determinants of predisposition of small scale business operators in Fagge local government area, Kano State, Nigeria, towards the PLS. Cross-sectional data from a sample of 291 small scale business operators was analyzed using logit and probit regression models. Empirical results reveal that while awareness and religion inclination positively drive interest towards the PLS, lending rate and collateral work against it. The paper, therefore, strongly recommends more advocacy campaigns and setting up of more Islamic banks in the country to cater for the financing and religious needs of SSBs in the study area.

Keywords: Islamic finance, logit and probit models, profit and loss sharing small scale businesses, finance, commerce

Procedia PDF Downloads 367

Authors: Hafiz Muhammad Adeel Sharif, Tian Li, Changping Li

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Recently, the emission of nitrogen-sulphur oxides (NOₓ, SO₂) has become a global issue and causing serious threats to health and the environment. Catalytic reduction of NOx and SOₓ gases into friendly gases is considered one of the best approaches. However, regeneration of the catalyst, higher bond-dissociation energy for NOx, i.e., 150.7 kcal/mol, escape of intermediate gas (N₂O, a greenhouse gas) with treated flue-gas, and limited activity of catalyst remains a great challenge. Here, a cheap, binderless naturally-extracted bass-wood thin carbon electrode (TCE) is presented, which shows excellent catalytic activity towards NOx reduction. The bass-wood carbonization at 900 ℃ followed by thermal activation in the presence of CO2 gas at 750 ℃. The thermal activation resulted in an increase in epoxy groups on the surface of the TCE and enhancement in the surface area as well as the degree of graphitization. The TCE unique 3D strongly inter-connected network through hierarchical micro/meso/macro pores that allow large electrode/electrolyte interface. Owing to these characteristics, the TCE exhibited excellent catalytic efficiency towards NOx (~83.3%) under ambient conditions and enhanced catalytic response under pH and sulphite exposure as well as excellent stability up to 168 hours. Moreover, a temperature-dependent activity trend was found where the highest catalytic activity was achieved at 80 ℃, beyond which the electrolyte became evaporative and resulted in a performance decrease. The designed electrocatalyst showed great potential for effective NOx-reduction, which is highly cost-effective, green, and sustainable.

Keywords: electrocatalyst, NOx-reduction, bass-wood electrode, integrated wet-scrubbing, sustainable

Procedia PDF Downloads 74

Authors: Abdelmalek Ouannou, Adil Brouri, Laila Kadi, Tarik

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Switched reluctance motor (SRM) has a major interest in a large domain as in electric vehicle driving because of its wide range of speed operation, high performances, low cost, and robustness to run under degraded conditions. The purpose of the paper is to develop a new analytical approach for modeling SRM parameters. Then, an identification scheme is proposed to obtain the SRM parameters. Since the SRM is featured by a highly nonlinear behavior, modeling these devices is difficult. Then, it is convenient to develop an accurate model describing the SRM. Furthermore, it is always operated in the magnetically saturated mode to maximize the energy transfer. Accordingly, it is shown that the SRM can be accurately described by a generalized polynomial Hammerstein model, i.e., the parallel connection of several Hammerstein models having polynomial nonlinearity. Presently an analytical identification method is developed using a chirp excitation signal. Afterward, the parameters of the obtained model have been determined using Finite Element Method analysis. Finally, in order to show the effectiveness of the proposed method, a comparison between the true and estimate models has been performed. The obtained results show that the output responses are very close.

Keywords: switched reluctance motor, swept-sine signal, generalized Hammerstein model, nonlinear system

Procedia PDF Downloads 232

Authors: Rizwan Rizwan

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This paper provides an overview of Federated Learning (FL) and its application in enhancing data security, privacy, and efficiency. FL utilizes three distinct architectures to ensure privacy is never compromised. It involves training individual edge devices and aggregating their models on a server without sharing raw data. This approach not only provides secure models without data sharing but also offers a highly efficient privacy--preserving solution with improved security and data access. Also we discusses various frameworks used in FL and its integration with machine learning, deep learning, and data mining. In order to address the challenges of multi--party collaborative modeling scenarios, a brief review FL scheme combined with an adaptive gradient descent strategy and differential privacy mechanism. The adaptive learning rate algorithm adjusts the gradient descent process to avoid issues such as model overfitting and fluctuations, thereby enhancing modeling efficiency and performance in multi-party computation scenarios. Additionally, to cater to ultra-large-scale distributed secure computing, the research introduces a differential privacy mechanism that defends against various background knowledge attacks.

Keywords: federated learning, differential privacy, gradient descent strategy, convergence, stability, threats

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Authors: D. G. Badagha, C. D. Modhera, S. A. Vasanwala

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There is a great challenge for civil engineering field to contribute in environment prevention by finding out alternatives of cement and natural aggregates. There is a problem of global warming due to cement utilization in concrete, so it is necessary to give sustainable solution to produce concrete containing waste. It is very difficult to produce designated grade of concrete containing different ingredient and water cement ratio including waste to achieve desired fresh and harden properties of concrete as per requirement and specifications. To achieve the desired grade of concrete, a number of trials have to be taken, and then after evaluating the different parameters at long time performance, the concrete can be finalized to use for different purposes. This research work is carried out to solve the problem of time, cost and serviceability in the field of construction. In this research work, artificial neural network introduced to fix proportion of concrete ingredient with 50% waste replacement for M20, M25, M30, M35, M40, M45, M50, M55 and M60 grades of concrete. By using the neural network, mix design of high performance concrete was finalized, and the main basic mechanical properties were predicted at 3 days, 7 days and 28 days. The predicted strength was compared with the actual experimental mix design and concrete cube strength after 3 days, 7 days and 28 days. This experimentally and neural network based mix design can be used practically in field to give cost effective, time saving, feasible and sustainable high performance concrete for different types of structures.

Keywords: artificial neural network, high performance concrete, rebound hammer, strength prediction

Procedia PDF Downloads 151

Authors: Tetsuji Tanaka, Jin Guo

Abstract:

The extant literature has not succeeded in uncovering the common determinants of price volatility transmissions of agricultural commodities from international to local markets, and further, has rarely investigated the role of self-sufficiency measures in the context of national food security. We analyzed various factors to determine the degree of price volatility transmissions of wheat, rice, and maize between world and domestic markets using GARCH models with dynamic conditional correlation (DCC) specifications and panel-feasible generalized least square models. We found that the grain autarky system has the potential to diminish volatility pass-throughs for three grain commodities. Furthermore, it was discovered that the substitutive commodity consumption behavior between maize and wheat buffers the volatility transmissions of both, but rice does not function as a transmission-relieving element, either for the volatilities of wheat or maize. The effectiveness of grain consumption substitution to insulate the pass-throughs from global markets is greater than that of cereal self-sufficiency. These implications are extremely beneficial for developing governments to protect their domestic food markets from uncertainty in foreign countries and as such, improves food security.

Keywords: food security, GARCH, grain self-sufficiency, volatility transmission

Procedia PDF Downloads 153