Search results for: forest wastes

Authors: Mahmoud Amer, Ibrahim El-Sharkawy, Shinichi Ookawara, Ahmed Elwardany

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Gasification and carbonization are two of the most common ways for biomass utilization. Both processes are using part of the waste to be accomplished, either by incomplete combustion or for heating for both gasification and carbonization, respectively. The focus of this paper is to minimize the part of the waste that is used for heating biomass for gasification and carbonization. This will occur by combining both gasifiers and carbonization reactors in a single unit to utilize the heat in the product biogas to heating up the wastes in the carbonization reactors. Three different designs are proposed for the combined gasification/carbonization (CGC) reactor. These include a parallel combination of two gasifiers and carbonized syngas, carbonizer and combustion chamber, and one gasifier, carbonizer, and combustion chamber. They are tested numerically using ANSYS Fluent Computational Fluid Dynamics to ensure homogeneity of temperature distribution inside the carbonization part of the CGC reactor. 2D simulations are performed for the three cases after performing both mesh-size and time-step independent solutions. The carbonization part is common among the three different cases, and the difference among them is how this carbonization reactor is heated. The simulation results showed that the first design could provide only partial homogeneous temperature distribution, not across the whole reactor. This means that the produced carbonized biomass will be reduced as it will only fill a specified height of the reactor. To keep the carbonized product production high, a series combination is proposed. This series configuration resulted in a uniform temperature distribution across the whole reactor as it has only one source for heat with no temperature distribution on any surface of the carbonization section. The simulations provided a satisfactory result that either the first parallel combination of gasifier and carbonization reactor could be used with a reduced carbonized amount or a series configuration to keep the production rate high.

Keywords: numerical simulation, carbonization, gasification, biomass, reactor

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Authors: Revoti Prasad Bora, Nikita Katyal

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Promotion is a key element in the retail business. Thus, analysis of promotions to quantify their effectiveness in terms of Revenue and/or Margin is an essential activity in the retail industry. However, measuring the sales/revenue uplift is based on estimations, as the actual sales/revenue without the promotion is not present. Further, the presence of Halo and Cannibalization in a multiple parallel promotions’ scenario complicates the problem. Calculating Baseline by considering inter-brand/competitor items or using Halo and Cannibalization's impact on Revenue calculations by considering Baseline as an interpretation of items’ unit sales in neighboring nonpromotional weeks individually may not capture the overall Revenue uplift in the case of multiple parallel promotions. Hence, this paper proposes a Machine Learning based method for calculating the Revenue uplift by considering the Halo and Cannibalization impact on the Baseline and the Revenue. In the first section of the proposed methodology, Baseline of an item is calculated by incorporating the impact of the promotions on its related items. In the later section, the Revenue of an item is calculated by considering both Halo and Cannibalization impacts. Hence, this methodology enables correct calculation of the overall Revenue uplift due a given promotion.

Keywords: Halo, Cannibalization, promotion, Baseline, temporary price reduction, retail, elasticity, cross price elasticity, machine learning, random forest, linear regression

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Authors: Emmanuel Osaze Oshoiribhor, Adetokunbo MacGregor John-Otumu

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In recent years, there has been a desire to forecast student academic achievement prior to graduation. This is to help them improve their grades, particularly for individuals with poor performance. The goal of this study is to employ supervised learning techniques to construct a predictive model for student academic achievement. Many academics have already constructed models that predict student academic achievement based on factors such as smoking, demography, culture, social media, parent educational background, parent finances, and family background, to name a few. This feature and the model employed may not have correctly classified the students in terms of their academic performance. This model is built using a logistic regression classifier with basic features such as the previous semester's course score, attendance to class, class participation, and the total number of course materials or resources the student is able to cover per semester as a prerequisite to predict if the student will perform well in future on related courses. The model outperformed other classifiers such as Naive bayes, Support vector machine (SVM), Decision Tree, Random forest, and Adaboost, returning a 96.7% accuracy. This model is available as a desktop application, allowing both instructors and students to benefit from user-friendly interfaces for predicting student academic achievement. As a result, it is recommended that both students and professors use this tool to better forecast outcomes.

Keywords: artificial intelligence, ML, logistic regression, performance, prediction

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Authors: Pamir Gogoi, Ratree Wayland

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This study investigates the acoustic measures of breathiness when coarticulated with nasality. The acoustic correlates of breathiness and nasality that has already been well established after years of empirical research. Some of these acoustic parameters - like low frequency peaks and wider bandwidths- are common for both nasal and breathy voice. Therefore, it is likely that these parameters interact when a sound is coarticulated with breathiness and nasality. This leads to the hypothesis that the acoustic parameters, which usually act as robust cues in differentiating between breathy and modal voice, might not be reliable cues for differentiating between breathy and modal voice when breathiness is coarticulated with nasality. The effect of nasality on the perception of breathiness has been explored in earlier studies using synthesized speech. The results showed that perceptually, nasality and breathiness do interact. The current study investigates if a similar pattern is observed in natural speech. The study is conducted on Marathi, an Indo-Aryan language which has a three-way contrast between nasality and breathiness. That is, there is a phonemic distinction between nasals, breathy voice and breathy-nasals. Voice quality parameters like – H1-H2 (Difference between the amplitude of first and second harmonic), H1-A3 (Difference between the amplitude of first harmonic and third formant, CPP (Cepstral Peak Prominence), HNR (Harmonics to Noise ratio) and B1 (Bandwidth of first formant) were extracted. Statistical models like linear mixed effects regression and Random Forest classifiers show that measures that capture the noise component in the signal- like CPP and HNR- can classify breathy voice from modal voice better than spectral measures when breathy voice is coarticulated with nasality.

Keywords: breathiness, marathi, nasality, voice quality

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Authors: Kavita Bodke

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Structural health monitoring (SHM) is experiencing growth, necessitating the development of distinct methodologies to address its expanding scope effectively. In this study, we developed automatic structure damage identification, which incorporates three unique types of a building’s structural integrity. The first pertains to the presence of fractures within the structure, the second relates to the issue of dampness within the structure, and the third involves corrosion inside the structure. This study employs image classification techniques to discern between intact and impaired structures within structural data. The aim of this research is to find automatic damage detection with the probability of each damage class being present in one image. Based on this probability, we know which class has a higher probability or is more affected than the other classes. Utilizing photographs captured by a mobile camera serves as the input for an image classification system. Image classification was employed in our study to perform multi-class and multi-label classification. The objective was to categorize structural data based on the presence of cracks, moisture, and corrosion. In the context of multi-class image classification, our study employed three distinct methodologies: Random Forest, Multilayer Perceptron, and CNN. For the task of multi-label image classification, the models employed were Rasnet, Xceptionet, and Inception.

Keywords: SHM, CNN, deep learning, multi-class classification, multi-label classification

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Authors: Raymond Xu, Cindy Jingru Wang

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Social media has become a globally connected and influencing platform. Social media data, such as tweets, can help predict the spread of pandemics and provide individuals and healthcare providers early warnings. Public psychological reactions and opinions can be efficiently monitored by AI models on the progression of dominant topics on Twitter. However, statistics show that as the coronavirus spreads, so does an infodemic of misinformation due to pandemic-related factors such as unemployment and lockdowns. Social media algorithms are often biased toward outrage by promoting content that people have an emotional reaction to and are likely to engage with. This can influence users’ attitudes and cause confusion. Therefore, social media is a double-edged sword. Combating fake news and biased content has become one of the essential tasks. This research analyzes the variety of methods used for fake news detection covering random forest, logistic regression, support vector machines, decision tree, naive Bayes, BoW, TF-IDF, LDA, CNN, RNN, LSTM, DeepFake, and hierarchical attention network. The performance of each method is analyzed. Based on these models’ achievements and limitations, a multi-dimensional AI framework is proposed to achieve higher accuracy in infodemic detection, especially pandemic-related news. The model is trained on contextual content, images, and news metadata.

Keywords: artificial intelligence, fake news detection, infodemic detection, image recognition, sentiment analysis

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Authors: Selman Cagman

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A detailed analysis is made within an organized industrial zone (OIZ) that has 1165 production facilities such as manufacturing of furniture, fabricated metal products (machinery and equipment), food products, plastic and rubber products, machinery and equipment, non-metallic mineral products, electrical equipment, textile products, and manufacture of wood and cork products. In this OIZ, a field study is done by choosing some facilities that can represent the whole OIZ sectoral distribution. In this manner, there are 207 facilities included to the site visit, and there is a 17 questioned survey carried out with each of them to assess their inputs, outputs, and waste amounts during manufacturing processes. The survey result identify that MDF/Particleboard and chipboard particles, textile, food, foam rubber, sludge (treatment sludge, phosphate-paint sludge, etc.), plastic, paper and packaging, scrap metal (aluminum shavings, steel shavings, iron scrap, profile scrap, etc.), slag (coal slag), ceramic fracture, ash from the fluidized bed are the wastes come from these facilities. As a result, there are 5 industrial symbiosis projects established with this study. One of the projects is a 2.840 kW capacity Integrated Biomass Based Waste Incineration-Energy Production Facility running on 35.000 tons/year of MDF particles and chipboard waste. Another project is a biogas plant with 225 tons/year whey, 100 tons/year of sesame husk, 40 tons/year of burnt wafer dough, and 2.000 tons/year biscuit waste. These two plants investment costs and operational costs are given in detail. The payback time of the 2.840 kW plant is almost 4 years and the biogas plant is around 6 years.

Keywords: industrial symbiosis, energy, biogas, waste to incineration

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Authors: Aree Thongpukdee, Chockpisit Thepsithar, Chuthalak Thammaso

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Use of plants grown in local area for edible has a long tradition in different culture. The indigenous knowledge such as usage of plants as vegetables by local people is risk to disappear when no records are done. In order to conserve and transfer this valuable heritage to the new generation, ethnobotanical study should be investigated and documented. The survey of vegetable plants traditionally used was carried out in the year 2012. Information was accumulated via questionnaires and oral interviewing from 100 people living in 36 villages of 9 districts in Amphoe Huai Mek, Kalasin, Thailand. Local plant names, utilized parts and preparation methods of the plants were recorded. Each mentioned plant species were collected and voucher specimens were prepared. A total of 55 vegetable plant species belonging to 34 families and 54 genera were identified. The plant habits were tree, shrub, herb, climber, and shrubby fern at 21.82%, 18.18%, 38.18%, 20.00% and 1.82% respectively. The most encountered vegetable plant families were Leguminosae (20%), Cucurbitaceae (7.27%), Apiaceae (5.45%), whereas families with 3.64% uses were Araceae, Bignoniaceae, Lamiaceae, Passifloraceae, Piperaceae and Solanaceae. The most common consumptions were fresh or brief boiled young shoot or young leaf as side dishes of ‘jaeo, laab, namprik, pon’ or curries. Most locally known vegetables included 45% of the studied plants which grow along road side, backyard garden, hedgerow, open forest and rice field.

Keywords: vegetable plants, ethnobotanical survey, Kalasin, Thailand

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Authors: Muhammad Ali

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Cyber-attacks and anomaly detection on the Internet of Things (IoT) infrastructure is emerging concern in the domain of data-driven intrusion. Rapidly increasing IoT risk is now making headlines around the world. denial of service, malicious control, data type probing, malicious operation, DDos, scan, spying, and wrong setup are attacks and anomalies that can affect an IoT system failure. Everyone talks about cyber security, connectivity, smart devices, and real-time data extraction. IoT devices expose a wide variety of new cyber security attack vectors in network traffic. For further than IoT development, and mainly for smart and IoT applications, there is a necessity for intelligent processing and analysis of data. So, our approach is too secure. We train several machine learning models that have been compared to accurately predicting attacks and anomalies on IoT systems, considering IoT applications, with ANOVA-based feature selection with fewer prediction models to evaluate network traffic to help prevent IoT devices. The machine learning (ML) algorithms that have been used here are KNN, SVM, NB, D.T., and R.F., with the most satisfactory test accuracy with fast detection. The evaluation of ML metrics includes precision, recall, F1 score, FPR, NPV, G.M., MCC, and AUC & ROC. The Random Forest algorithm achieved the best results with less prediction time, with an accuracy of 99.98%.

Keywords: machine learning, analysis of variance, Internet of Thing, network security, intrusion detection

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Authors: Mulubrhan Berihu, Supratic Gupta, Zena Gebriel

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Due to the design versatility, availability, and cost efficiency, concrete is continuing to be the most used construction material on earth. However, the production of Portland cement, the primary component of concrete mix is causing to have a serious effect on environmental and economic impacts. This shows there is a need to study using of supplementary cementitious materials (SCMs). The most commonly used supplementary cementitious materials are wastes and the use of these industrial waste products has technical, economical and environmental benefits besides the reduction of CO2 emission from cement production. The study aims to document the effect on strength property of concrete due to use of low cement by maximizing supplementary cementitious materials like fly ash or marble powder. Based on the different mix proportion of pozzolana and marble powder a range of mix design was formulated. The first part of the project is to study the strength of low cement concrete using fly ash replacement experimentally. The test results showed that using up to 85 kg/m3 of cement is possible for plain concrete works like hollow block concrete to achieve 9.8 Mpa and the experimental results indicates that strength is a function of w/b. In the second part a new set of mix design has been carried out with fly ash and marble powder to study the strength of both binary and ternary mixes. In this experimental study, three groups of mix design (c+FA, c+FA+m and c+m), four sets of mixes for each group were taken up. Experimental results show that c+FA has maintained the best strength and impermeability whereas c+m obtained less compressive strength, poorer permeability and split tensile strength. c+FA shows a big difference in gaining of compressive strength from 7 days to 28 days compression strength compared to others and this obviously shows the slow rate of hydration of fly ash concrete. As the w/b ratio increases the strength decreases significantly. At the same time higher permeability has been seen in the specimens which were tested for three hours than one hour.

Keywords: efficiency factor, cement content, compressive strength, mix proportion, w/c ratio, water permeability, SCMs

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Authors: Walid Moudani

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The healthcare environment is generally perceived as being information rich yet knowledge poor. However, there is a lack of effective analysis tools to discover hidden relationships and trends in data. In fact, valuable knowledge can be discovered from application of data mining techniques in healthcare system. In this study, a proficient methodology for the extraction of significant patterns from the coronary heart disease warehouses for heart attack prediction, which unfortunately continues to be a leading cause of mortality in the whole world, has been presented. For this purpose, we propose to enumerate dynamically the optimal subsets of the reduced features of high interest by using rough sets technique associated to dynamic programming. Therefore, we propose to validate the classification using Random Forest (RF) decision tree to identify the risky heart disease cases. This work is based on a large amount of data collected from several clinical institutions based on the medical profile of patient. Moreover, the experts’ knowledge in this field has been taken into consideration in order to define the disease, its risk factors, and to establish significant knowledge relationships among the medical factors. A computer-aided system is developed for this purpose based on a population of 525 adults. The performance of the proposed model is analyzed and evaluated based on set of benchmark techniques applied in this classification problem.

Keywords: multi-classifier decisions tree, features reduction, dynamic programming, rough sets

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Authors: Adeola Ibukunoluwa Samuel, Afsoon Kazerouni

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Industrial mineral kaolin has many favourable properties such as colour, shape, softness, non-abrasiveness, natural whiteness, as well as chemical stability. It occurs extensively in North of Bedford road Grahamstown, South Africa. The relationship between both the physical and chemical properties as lead to its application in the production of certain industrial products which are used by the public; this includes the prospect of production of paper, ceramics, rubber, paint, and plastics. Despite its interesting economic potentials, kaolinite clay mineral remains undermined, and this is threatening its sustainability in the mineral industry. This research study focuses on a detailed evaluation of the kaolinite mineral and possible ways to increase its lifespan in the industry. The methods employed for this study includes petrographic microscopy analysis, X-ray powder diffraction analysis (XRD), and proper field reconnaissance survey. Results emanating from this research include updated geological information on Grahamstown. Also, mineral transformation phases such as quartz, kaolinite, calcite and muscovite were identified in the clay samples. Petrographic analysis of the samples showed that the study area has been subjected to intense tectonic deformation and cement replacement. Also, different dissolution patterns were identified on the Grahamstown kaolinitic clay deposits. Hence incorporating analytical studies and data interpretations, possible ways such as the establishment of processing refinery near mining plants, which will, in turn, provide employment for the locals and land reclamation is suggested. In addition, possible future sustainable industrial applications of the clay minerals seem to be possible if additives, cellulosic wastes are used to alter the clay mineral.

Keywords: kaolinite, industrial use, sustainability, Grahamstown, clay minerals

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Authors: Gatot Dwi Hendro, Hayyan ul Haq

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This work aims to explore the strategy in embodying the ecological validity in creating the sustainability of public policy, particularly in strengthening the green economy in Indonesia. This green economy plays an important role in supporting the national development in Indonesia, as it is a part of the national policy that posits the primary priority in Indonesian governance. The green economy refers to the national development covering strategic natural resources, such as mining, gold, oil, coal, forest, water, marine, and the other supporting infrastructure for products and distribution, such as fabrics, roads, bridges, and so forth. Thus, all activities in those national development should consider the sustainability. This sustainability requires the strong commitment of the national and regional government, as well as the local governments to put the ecology as the main requirement for issuing any policy, such as licence in mining production, and developing and building new production and supporting infrastructures for optimising the national resources. For that reason this work will focus on the strategy how to embody the ecological values and norms in the public policy. In detail, this work will offer the method, i.e. legal techniques, in visualising and embodying the norms and public policy that valid ecologically. This ecological validity is required in order to maintain and sustain our collective life.

Keywords: ecological validity, sustainable development, coherence, Indonesian Pancasila values, environment, marine

Procedia PDF Downloads 487

Authors: C. Girometta, S. Babbini, R. M. Baiguera, D. S. Branciforti, M. Cartabia, D. Dondi, M. Pellegrini, A. M. Picco, E. Savino

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Interest on wood decay fungi (WDF) has been increasing in the last year's thanks to the potentiality of this kind of fungi; research on new WDF strains has increased as well thus pointing out the key role of the culture collections. One of the most recent biotechnological application of WDF is the development of novel materials from natural or recycled resources. Based on different combinations of fungal species, substrate, and processing treatment involved (e.g. heat pressing), it is possible to achieve a wide variety of materials with different features useful for many industrial applications: from packaging to thermal and acoustic insulation. In comparison with the conventional ones, these materials represent a 100% natural and compostable alternative involving low amounts of energy in the production process. The purpose of the present work was to isolate and select WDF strains able to colonize and degrade different plant wastes thus producing a fungal biomass shapeable to achieve bio-composite materials. Strains were selected within the mycological culture collection of Pavia University (MicUNIPV, over 300 strains of WDF). The selected strains have been investigated with regards their ability to colonize and degrade plant residues from the local major cultivations (e.g. poplar, alfalfa, maize, rice, and wheat) and produce the fungal biomass. The degradation of the substrate was assessed by Thermogravimetric analysis (TGA) and Fourier Transform Infrared Spectroscopy (FTIR). Chemical characterization confirmed that TGA and FTIR are complementary techniques able to provide quality-quantitative information on compositional and structural variation that occurs during the transformation from the substrate to the bio-composite material. This pilot study provides a fundamental step to tune further applications in fungus-residues composite biomaterials.

Keywords: bio-composite material, lignocellulosic residues, sustainable materials, wood decay fungi

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Authors: Neha Budhwani

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Polycyclic aromatic hydrocarbons (PAHs) are formed during the pyrolysis of scrap tyres to produce tyre pyrolytic oil (TPO). Due to carcinogenic, mutagenic, and toxic properties PAHs are priority pollutants. Hence it is essential to remove PAHs from TPO before utilising TPO as a petroleum fuel alternative (to run the engine). Agricultural wastes have promising future to be utilized as biosorbent due to their cost effectiveness, abundant availability, high biosorption capacity and renewability. Various low cost adsorbents were prepared from natural sources. Uptake of PAHs present in tyre pyrolytic oil was investigated using various low-cost adsor¬bents of natural origin including sawdust (shiham), coconut fiber, neem bark, chitin, activated charcol. Adsorption experiments of different PAHs viz. naphthalene, acenaphthalene, biphenyl and anthracene have been carried out at ambient temperature (25°C) and at pH 7. It was observed that for any given PAH, the adsorption capacity increases with the lignin content. Freundlich constant kf and 1/n have been evaluated and it was found that the adsorption isotherms of PAHs were in agreement with a Freundlich model, while the uptake capacity of PAHs followed the order: activated charcoal> saw dust (shisham) > coconut fiber > chitin. The partition coefficients in acetone-water, and the adsorption constants at equilibrium, could be linearly correlated with octanol–water partition coefficients. It is observed that natural adsorbents are good alternative for PAHs removal. Sawdust of Dalbergia sissoo, a by-product of sawmills was found to be a promising adsorbent for the removal of PAHs present in TPO. It is observed that adsorbents studied were comparable to those of some conventional adsorbents.

Keywords: natural adsorbent, PAHs, TPO, coconut fiber, wood powder (shisham), naphthalene, acenaphthene, biphenyl and anthracene

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Authors: Kong Wing Man

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The purpose of this paper is to review the waste generation and management in different parts of the world. It is undeniable that waste generation and management has become an alarming environmental issue. Solid waste generation links inextricably to the degree of industrialization and economic development. Urbanization increases with the economic wealth of the countries. As the income of people and standard of living enhances, so does their consumption of goods and services, leading to a corresponding increase in waste generation. Based on the latest statistics from What A Waste Report published by World Bank (2012), it is estimated that the current global Municipal Solid Waste (MSW) generation levels are about 1.3 billion tonnes per year (1.2 kg per capita per day). By 2050, it is projected that the waste generation will be doubled. Although many waste collection practices have been implemented in various countries, the amount of waste generation keeps increasing. An integrated solid waste management is needed in order to reduce the continuous significant increase in waste generation rates. Although many countries have introduced and implemented the 3Rs strategy and landfill, however, these are only the ways to diverse waste, but cannot reduce the volume. Instead, the advanced thermal treatment technology, incineration, can reduce up to 90% volume of disposed waste prior to dispose at landfills is discussed. Sweden and Tokyo were chosen as case studies, which provide an overview of the municipal solid waste management system. With the condition of escalating amount of wastes generated, it is crucial to build incinerators to relief pressing needs of landfill. Two solutions are proposed to minimize waste generation, including one incineration in one city and several small incinerators in different cities. While taking into consideration of a sustainable model and the perspectives of all stakeholders, building several incinerators at different cities and different sizes would be the best option to reduce waste. Overall, the solution to the global solid waste management should be a holistic approach with the involvement of both government and citizens.

Keywords: Incineration, Municipal Solid Waste, Thermal Treatment, Waste generation

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Authors: Sagir M. Yusuf, Chris Baber

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In this paper, we describe how Bayesian inferential reasoning will contributes in obtaining a well-satisfied prediction for Distributed Constraint Optimization Problems (DCOPs) with uncertainties. We also demonstrate how DCOPs could be merged to multi-agent knowledge understand and prediction (i.e. Situation Awareness). The DCOPs functions were merged with Bayesian Belief Network (BBN) in the form of situation, awareness, and utility nodes. We describe how the uncertainties can be represented to the BBN and make an effective prediction using the expectation-maximization algorithm or conjugate gradient descent algorithm. The idea of variable prediction using Bayesian inference may reduce the number of variables in agents’ sampling domain and also allow missing variables estimations. Experiment results proved that the BBN perform compelling predictions with samples containing uncertainties than the perfect samples. That is, Bayesian inference can help in handling uncertainties and dynamism of DCOPs, which is the current issue in the DCOPs community. We show how Bayesian inference could be formalized with Distributed Situation Awareness (DSA) using uncertain and missing agents’ data. The whole framework was tested on multi-UAV mission for forest fire searching. Future work focuses on augmenting existing architecture to deal with dynamic DCOPs algorithms and multi-agent information merging.

Keywords: DCOP, multi-agent reasoning, Bayesian reasoning, swarm intelligence

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Authors: A. Farhad Sadri, B. Ali Farhadi, C. Nasim Shalamzari

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Waste management is a principle of urban and environmental governance. Waste management in Tehran metropolitan requires good strategies for better governance. Using of good urban governance principles together with eight main indexes can be an appropriate base for this aim. One of the reasonable tools in this field is usage of SWOT methods which provides possibility of comparing the opportunities, threats, weaknesses, and strengths by using IFE and EFE matrixes. The results of the above matrixes, respectively 2.533 and 2.403, show that management system of Tehran metropolitan wastes has performed weak regarding to internal factors and has not have good performance regarding using the opportunities and dealing with threats. In this research, prioritizing and describing the real value of each 24 strategies in waste management in Tehran metropolitan have been surveyed considering good governance derived from Quantitative Strategic Planning Management (QSPM) by using Kolomogrof-Smirnoff by 1.549 and significance level of 0.073 in order to define normalization of final values and all of the strategies utilities and Variance Analysis of ANOVA has been calculated for all SWOT strategies. Duncan’s test results regarding four WT, ST, WO, and SO strategies show no significant difference. In addition to mean comparison by Duncan method in this research, LSD (Lowest Significant Difference test) has been used by probability of 5% and finally, 7 strategies and final model of Tehran metropolitan waste management strategy have been defined. Increasing the confidence of people with transparency of budget, developing and improving the legal structure (rule-oriented and law governance, more responsibility about requirements of private sectors, increasing recycling rates and real effective participation of people and NGOs to improve waste management (contribution) and etc, are main available strategies which have been achieved based on good urban governance management principles.

Keywords: waste, strategy, environmental management, urban good governance, SWOT

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Authors: Abdullahi Jibrin, Aishetu Abdulkadir

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The development of allometric models is crucial to accurate forest biomass/carbon stock assessment. The aim of this study was to develop a set of biomass prediction models that will enable the determination of total tree aboveground biomass for savannah woodland area in Niger State, Nigeria. Based on the data collected through biometric measurements of 1816 trees and destructive sampling of 36 trees, five species specific and one site specific models were developed. The sample size was distributed equally between the five most dominant species in the study site (Vitellaria paradoxa, Irvingia gabonensis, Parkia biglobosa, Anogeissus leiocarpus, Pterocarpus erinaceous). Firstly, the equations were developed for five individual species. Secondly these five species were mixed and were used to develop an allometric equation of mixed species. Overall, there was a strong positive relationship between total tree biomass and the stem diameter. The coefficient of determination (R2 values) ranging from 0.93 to 0.99 P < 0.001 were realised for the models; with considerable low standard error of the estimates (SEE) which confirms that the total tree above ground biomass has a significant relationship with the dbh. The F-test value for the biomass prediction models were also significant at p < 0.001 which indicates that the biomass prediction models are valid. This study recommends that for improved biomass estimates in the study site, the site specific biomass models should preferably be used instead of using generic models.

Keywords: allometriy, biomass, carbon stock , model, regression equation, woodland, inventory

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Authors: A. Bouzekri, H. Benmassaud

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Aurès region is one of the arid and semi-arid areas that have suffered climate crises and overexploitation of natural resources they have led to significant land degradation. The use of remote sensing data allowed us to analyze the land and its spatiotemporal changes in the Aurès between 1987 and 2013, for this work, we adopted a method of analysis based on the exploitation of the images satellite Landsat TM 1987 and Landsat OLI 2013, from the supervised classification likelihood coupled with field surveys of the mission of May and September of 2013. Using ENVI EX software by the superposition of the ground cover maps from 1987 and 2013, one can extract a spatial map change of different land cover units. The results show that between 1987 and 2013 vegetation has suffered negative changes are the significant degradation of forests and steppe rangelands, and sandy soils and bare land recorded a considerable increase. The spatial change map land cover units between 1987 and 2013 allows us to understand the extensive or regressive orientation of vegetation and soil, this map shows that dense forests give his place to clear forests and steppe vegetation develops from a degraded forest vegetation and bare, sandy soils earn big steppe surfaces that explain its remarkable extension. The analysis of remote sensing data highlights the profound changes in our environment over time and quantitative monitoring of the risk of desertification.

Keywords: remote sensing, spatiotemporal, land use, Aurès

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Authors: S. Niamkaeo, O. Robert, O. Chaowalit

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In this investigation, we focus on discovering spatial relationship of drug smuggling along the northern border of Thailand. Thailand is no longer a drug production site, but Thailand is still one of the major drug trafficking hubs due to its topographic characteristics facilitating drug smuggling from neighboring countries. Our study areas cover three districts (Mae-jan, Mae-fahluang, and Mae-sai) in Chiangrai city and four districts (Chiangdao, Mae-eye, Chaiprakarn, and Wienghang) in Chiangmai city where drug smuggling of methamphetamine crystal and amphetamine occurs mostly. The data on drug smuggling incidents from 2011 to 2017 was collected from several national and local published news. Geo-spatial drug smuggling database was prepared. Decision tree algorithm was applied in order to discover the spatial relationship of factors related to drug smuggling, which was converted into rules using rule-based system. The factors including land use type, smuggling route, season and distance within 500 meters from check points were found that they were related to drug smuggling in terms of rules-based relationship. It was illustrated that drug smuggling was occurred mostly in forest area in winter. Drug smuggling exhibited was discovered mainly along topographic road where check points were not reachable. This spatial relationship of drug smuggling could support the Thai Office of Narcotics Control Board in surveillance drug smuggling.

Keywords: decision tree, drug smuggling, Geographic Information System, GIS knowledge discovery, rule-based system

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Authors: Vishaal Subramanian, Annuatha Vinod Kumar, Santosh Kumar Budankayala, M. Senthil Kumar

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This paper discusses the aerodynamic and structural design, simulation and optimization of a mini-High Altitude Long Endurance (HALE) UAV. The applications of this mini HALE UAV vary from aerial topological surveys, quick first aid supply, emergency medical blood transport, search and relief activates to border patrol, surveillance and estimation of forest fire progression. Although classified as a mini UAV according to UVS International, our design is an amalgamation of the features of ‘mini’ and ‘HALE’ categories, combining the light weight of the ‘mini’ and the high altitude ceiling and endurance of the HALE. Designed with the idea of implementation in India, it is in strict compliance with the UAS rules proposed by the office of the Director General of Civil Aviation. The plane can be completely automated or have partial override control and is equipped with an Infra-Red camera and a multi coloured camera with on-board storage or live telemetry, GPS system with Geo Fencing and fail safe measures. An additional of 1.5 kg payload can be attached to three major hard points on the aircraft and can comprise of delicate equipment or releasable payloads. The paper details the design, optimization process and the simulations performed using various software such as Design Foil, XFLR5, Solidworks and Ansys.

Keywords: aircraft, endurance, HALE, high altitude, long range, UAV, unmanned aerial vehicle

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Authors: Mailin Misson, Bo Jin, Sheng Dai, Hu Zhang

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Advances in biotechnology have witnessed a growing interest in enzyme applications for the development of green and sustainable bio processes. While known as powerful bio catalysts, enzymes are no longer of economic value when extended to large commercialization. Alternatively, immobilization technology allows enzyme recovery and continuous reuse which subsequently compensates high operating costs. Employment of enzymes on nano structured materials has been recognized as a promising approach to enhance enzyme catalytic performances. High porosity, inter connectivity and self-assembling behaviors endow nano fibers as exciting candidate for enzyme carrier in bio reactor systems. In this study, nano fibers were successfully fabricated via electro spinning system by optimizing the polymer concentration (10-30 %, w/v), applied voltage (10-30 kV) and discharge distance (11-26 cm). Microscopic images have confirmed the quality as homogeneous and good fiber alignment. The nano fibers surface was modified using strong oxidizing agent to facilitate bio molecule binding. Bovine serum albumin and β-galactosidase enzyme were employed as model bio catalysts and immobilized onto the oxidized surfaces through covalent binding. Maximum enzyme adsorption capacity of the modified nano fibers was 3000 mg/g, 3-fold higher than the unmodified counterpart (1000 mg/g). The highest immobilization yield was 80% and reached the saturation point at 2 mg/ml of enzyme concentration. The results indicate a significant increase of activity retention by the enzyme-bound modified nano fibers (80%) as compared to the nascent one (60%), signifying excellent enzyme-nano carrier bio compatibility. The immobilized enzyme was further used for the bio conversion of dairy wastes into value-added products. This study demonstrates great potential of acid-modified electrospun polystyrene nano fibers as enzyme carriers.

Keywords: immobilization, enzyme, nanocarrier, nanofibers

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Authors: Nuria Vargas, Frances Rodriguez

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Neotropical medium cities have opportunities to develop in a good manner. Xalapa City (Veracruz capital, Mexico) and its metropolitan region, near to the Gulf of Mexico, has already <1 million inhabitants, a medium city size, but it’s growing rapidly as several cities in Latin America. Inside a landscape where it had been a forest cloud and coffee land, emerges the city with an irregular topography. The rapid grow of the urbanization and the loss of vegetation has result in a change on the climate parameters. Frequently warms spells, floods and landslides had been impacted last 2 decades, also a higher incidence of dengue and diarrhea is mentioned in the region. Therefore, the analysis of hydrometeorological events is crucial to understand the role they play in its problem. The urbanization and others radiative forces has created a modulation that can explain the decadal climate changes on the Xalapa region. The Atlantic Multidecadal Oscillation directly influences the temperature and precipitation of the region, even more than climate change does. The total effect of these drivers can create a significant context that origin more risk. However, the most policies frequently consider only the climate change as a principal factor, but other drivers are important to consider and evaluate for the implementation of actions that improve our ambient and cities, in a context of climate change. Medium-sized cities could create better conditions for future citizens, preventing with urban planning that considers possible risks associated with weather and climate.

Keywords: natural variability, urbanization, atlantic multidecadal oscillation, land use changes

Procedia PDF Downloads 64

Authors: Molin A., De Ramos J. M., Cadion L. G., Pico R. L.

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Malnutrition and food insecurity represent significant global challenges affecting millions of individuals, particularly in low-income and developing regions. The researchers created a solar-powered enzymatic composting bioreactor with an Arduino-based monitoring system for pH, humidity, and temperature. It manages mixed municipal solid wastes incorporating industrial enzymes and whey additives for accelerated composting and minimized carbon footprint. Within 15 days, the bioreactor yielded 54.54% compost compared to 44.85% from traditional methods, increasing yield by nearly 10%. Tests showed that the bioreactor compost had 4.84% NPK, passing metal analysis standards, while the traditional pit compost had 3.86% NPK; both are suitable for agriculture. Statistical analyses, including ANOVA and Tukey's HSD test, revealed significant differences in agricultural yield across different compost types based on leaf length, width, and number of leaves. The study compared the effects of different composts on Brassica rapa subsp. Chinesis (Petchay) and Brassica juncea (Mustasa) plant growth. For Pechay, significant effects of compost type on plant leaf length (F(5,84) = 62.33, η² = 0.79) and leaf width (F(5,84) = 12.35, η² = 0.42) were found. For Mustasa, significant effects of compost type on leaf length (F(4,70) = 20.61, η² = 0.54), leaf width (F(4,70) = 19.24, η² = 0.52), and number of leaves (F(4,70) = 13.17, η² = 0.43) were observed. This study explores the effectiveness of the enzymatic composting bioreactor and its viability in promoting carbon farming as a solution to food insecurity and malnutrition.

Keywords: malnutrition, food insecurity, enzymatic composting bioreactor, arduino-based monitoring system, enzymes, carbon farming, whey additive, NPK level

Procedia PDF Downloads 59

Authors: Vu H. N. Khue, Marian Pavelka, Georg Jocher, Jiří Dušek, Le T. Son, Bui T. An, Ho Q. Bang, Pham Q. Huong

Abstract:

Agriculture is an important economic sector of Vietnam, the most popular of which is wet rice cultivation. These activities are also known as the main contributor to the national greenhouse gas. In order to understand more about greenhouse gas exchange in these activities and to investigate the factors influencing carbon cycling and sequestration in these types of ecosystems, since 2019, the first eddy covariance station has been installed in a paddy field in Long An province, Mekong Delta. The station was equipped with state-of-the-art equipment for CO₂ and CH₄ gas exchange and micrometeorology measurements. In this study, data from the station was processed following the ICOS recommendations (Integrated Carbon Observation System) standards for CO₂, while CH₄ was manually processed and gap-filled using a random forest model from methane-gapfill-ml, a machine learning package, as there is no standard method for CH₄ flux gap-filling yet. Finally, the carbon equivalent (Ce) balance based on CO₂ and CH₄ fluxes was estimated. The results show that in 2020, even though a new water management practice - alternate wetting and drying - was applied to reduce methane emissions, the paddy field released 928 g Cₑ.m⁻².yr⁻¹, and in 2021, it was reduced to 707 g Cₑ.m⁻².yr⁻¹. On a provincial level, rice cultivation activities in Long An, with a total area of 498,293 ha, released 4.6 million tons of Cₑ in 2020 and 3.5 million tons of Cₑ in 2021.

Keywords: eddy covariance, greenhouse gas, methane, rice cultivation, Mekong Delta

Procedia PDF Downloads 142

Authors: Vera Marcantonio, Marcello De Falco, Mauro Capocelli, Álvaro Amado-Fierro, Teresa A. Centeno, Enrico Bocci

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Concerns about energy security, energy prices, and climate change led scientific research towards sustainable solutions to fossil fuel as renewable energy sources coupled with hydrogen as an energy vector and carbon capture and conversion technologies. Among the technologies investigated in the last decades, biomass gasification acquired great interest owing to the possibility of obtaining low-cost and CO₂ negative emission hydrogen production from a large variety of everywhere available organic wastes. Upstream and downstream treatment were then studied in order to maximize hydrogen yield, reduce the content of organic and inorganic contaminants under the admissible levels for the technologies which are coupled with, capture, and convert carbon dioxide. However, studies which analyse a whole process made of all those technologies are still missing. In order to fill this lack, the present paper investigated the coexistence of hydrothermal carbonization (HTC), sorption enhance gasification (SEG), hot gas cleaning (HGC), and CO₂ conversion by dielectric barrier discharge (DBD) plasma reactor for H₂ production from biomass waste by means of Aspen Plus software. The proposed model aimed to identify and optimise the performance of the plant by varying operating parameters (such as temperature, CaO/biomass ratio, separation efficiency, etc.). The carbon footprint of the global plant is 2.3 kg CO₂/kg H₂, lower than the latest limit value imposed by the European Commission to consider hydrogen as “clean”, that was set to 3 kg CO₂/kg H₂. The hydrogen yield referred to the whole plant is 250 gH₂/kgBIOMASS.

Keywords: biomass gasification, hydrogen, aspen plus, sorption enhance gasification

Procedia PDF Downloads 81

Authors: Diane H. Dotson, Shari L. Rodriguez

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Habitat for predators is on the decline worldwide, which often brings humans and predators into conflict over remaining shared space and common resources. While the direct impacts of human predator conflict on humans (i.e., attacks on livestock or humans resulting in injury or death) are well documented, the indirect impacts of conflict on humans (i.e., downstream effects such as fear, stress, opportunity costs, PTSD) have not been addressed. We interviewed 437 people living in 54 villages on the periphery of Kanha National Park, India, to assess the amount and severity of direct and indirect impacts of predator conflict. ​While 58% of livestock owners believed that predator attacks on livestock guards occurred frequently and 62% of those who collect forest products believed that predator attacks on those collecting occurred frequently, less than 20% of all participants knew of someone who had experienced an attack. Data related to indirect impacts suggest that such impacts are common; 76% of participants indicated they were afraid a predator will physically injure them. Livestock owners reported that livestock guarding took time away from their primary job (61%) and getting enough sleep (73%), and believed that it increased their vulnerability to illnesses (80%). These results suggest that the perceptions of risk of predator attack are likely inflated, yet the costs of human predator impacts may be substantially higher than previously estimated, particularly related to human well-being, making the implementation of appropriate and effective conservation and conflict mitigation strategies and policies increasingly urgent.

Keywords: direct impacts, indirect impacts, human-predator conflict, India

Procedia PDF Downloads 156

Authors: Abraham Addo-Bediako

Abstract:

Many freshwater ecosystems are facing immense pressure from anthropogenic activities, such as agricultural, industrial and mining. Trace metal pollution in freshwater ecosystems has become an issue of public health concern due to its toxicity and persistence in the environment. Trace elements pose a serious risk not only to the environment and aquatic biota but also humans. Chromium is one of such trace elements and its pollution in surface waters and groundwaters represents a serious environmental problem. In South Africa, agriculture, mining, industrial and domestic wastes are the main contributors to chromium discharge in rivers. The common forms of chromium are chromium (III) and chromium (VI). The latter is the most toxic because it can cause damage to human health. The aim of the study was to assess the contamination of chromium in the water and sediments of two rivers in the Steelpoort River sub-catchment of the Olifants River Basin, South Africa and human health risk. The concentration of Cr was analyzed using inductively coupled plasma–optical emission spectrometry (ICP-OES). The concentration of the metal was found to exceed the threshold limit, mainly in areas of high human activities. The hazard quotient through ingestion exposure did not exceed the threshold limit of 1 for adults and children and cancer risk for adults and children computed did not exceed the threshold limit of 10-4. Thus, there is no potential health risk from chromium through ingestion of drinking water for now. However, with increasing human activities, especially mining, the concentration could increase and become harmful to humans who depend on rivers for drinking water. It is recommended that proper management strategies should be taken to minimize the impact of chromium on the rivers and water from the rivers should properly be treated before domestic use.

Keywords: land use, health risk, metal pollution, water quality

Procedia PDF Downloads 88

Authors: M. Devaki, K. B. Jayanthi

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The identification of water bodies from satellite images has recently received a great deal of attention. Different methods have been developed to distinguish water bodies from various satellite images that vary in terms of time and space. Urban water identification issues body manifests in numerous applications with a great deal of certainty. There has been a sharp rise in the usage of satellite images to map natural resources, including urban water bodies and forests, during the past several years. This is because water and forest resources depend on each other so heavily that ongoing monitoring of both is essential to their sustainable management. The relevant elements from satellite pictures have been chosen using a variety of techniques, including machine learning. Then, a convolution neural network (CNN) architecture is created that can identify a superpixel as either one of two classes, one that includes water or doesn't from input data in a complex metropolitan scene. The deep learning technique, CNN, has advanced tremendously in a variety of visual-related tasks. CNN can improve classification performance by reducing the spectral-spatial regularities of the input data and extracting deep features hierarchically from raw pictures. Calculate the water body using the satellite image's resolution. Experimental results demonstrate that the suggested method outperformed conventional approaches in terms of water extraction accuracy from remote-sensing images, with an average overall accuracy of 97%.

Keywords: water body, Deep learning, satellite images, convolution neural network

Procedia PDF Downloads 90