Search results for: weed infestation forecast

Authors: Timothy J. Considine, Edward J. M. Manderson

Abstract:

This paper develops an econometric forecasting system of energy demand coupled with engineering-economic models of energy supply. The framework is used to quantify the impact of state-level renewable portfolio standards (RPSs) achieved predominately with solar generation on electricity rates, electricity consumption, and environmental quality. We perform the analysis using Arizona’s RPS as a case study. We forecast energy demand in Arizona out to 2035, and find by this time the state will require an additional 35 million MWh of electricity generation. If Arizona implements its RPS when supplying this electricity demand, we find there will be a substantial increase in electricity rates (relative to a business-as-usual scenario of reliance on gas-fired generation). Extending the current regime of tax credits can greatly reduce this increase, at the taxpayers’ expense. We find that by 2025 Arizona’s RPS will implicitly abate carbon dioxide emissions at a cost between $101 and $135 per metric ton, and by 2035 abatement costs are between $64 and $112 per metric ton (depending on the future evolution of nature gas prices).

Keywords: electricity demand, renewable portfolio standard, solar, carbon dioxide

Procedia PDF Downloads 457

Authors: Philip Vercoe, Ali Hardan

Abstract:

Samples of 29 Australian wild natives and exotic plants were tested in vitro batch rumen culture system for their methanogenic characteristics and potential usage as feed or antimicrobial to enhance sustainable livestock ruminant production system. The plants were tested for their in vitro rumen fermentation end products properties which include: methane production, total gas pressure, concentrations of total volatile fatty acids, ammonia, and acetate to propionate ratio. All of the plants were produced less methane than the positive control (i.e., oaten chaff) in vitro. Nearly 50 % of plants inhibiting methane by over 50% in comparison to the control. Eremophila granitica had the strongest inhibitory effect about 92 % on methane production comparing with oaten chaff. The exotic weed Arctotheca calendula (Capeweed) had the highest concentration of volatile fatty acids production as well as the highest in total gas pressure among all plants and the control. Some of the acacia species have the lowest production of total gas pressure. The majority of the plants produced more ammonia than the oaten chaff control. The plant species that produced the most ammonia was Codonocarpus cotinifolius, producing over 3 times as much methane as oaten chaff control while the lowest was Eremophila galeata. There was strong positive correlation between methane production and total gas production as well as between total gas production and the concentration of VFA produced with R² = 0.74, R² = 0.84, respectively. While there was weak positive correlation between methane production and the acetate to propionate ratio as well as between the concentration of VFA produced and methane production with R² = 0.41, R² = 0.52, respectively.

Keywords: in vitro Rumen Fermentation, methane, wild Australian native plants, forages

Procedia PDF Downloads 322

Authors: Azzedine Hamza, Chouaib Chakour, Messaoud Ramdani

Abstract:

Energy demand prediction plays a crucial role in achieving next-generation power systems for agricultural greenhouses. As a result, high prediction quality is required for efficient smart grid management and therefore low-cost energy consumption. The aim of this paper is to investigate the effectiveness of a hybrid data-driven model in day-ahead energy demand prediction. The proposed model consists of Discrete Wavelet Transform (DWT), and Adaptive Neuro-Fuzzy Inference System (ANFIS). The DWT is employed to decompose the original signal in a set of subseries and then an ANFIS is used to generate the forecast for each subseries. The proposed hybrid method (DWT-ANFIS) was evaluated using a greenhouse energy demand data for a week and compared with ANFIS. The performances of the different models were evaluated by comparing the corresponding values of Mean Absolute Percentage Error (MAPE). It was demonstrated that discret wavelet transform can improve agricultural greenhouse energy demand modeling.

Keywords: wavelet transform, ANFIS, energy consumption prediction, greenhouse

Procedia PDF Downloads 58

Authors: Angel Champion, Sadia Kanwal, Rafat Siddiqui

Abstract:

Harnessing phytochemicals from plant sources presents a novel opportunity to prevent or treat malignant diseases, including breast cancer. Chemotherapy lacks precision in targeting cancerous cells while sparing normal cells, but a phytopharmaceutical approach may offer a solution. Dandelion, a common weed plant, is rich in phytochemicals and provides a safer, more cost-effective alternative with lower toxicity than traditional pharmaceuticals for conditions such as breast cancer. In this study, an in-vitro experiment will be conducted using the ethanol extract of Dandelion on triple-negative MDA-231 breast cancer cell lines. The polyphenolic analysis revealed that the Dandelion extract, particularly from the root and leaf (both cut and sifted), had the most potent antioxidant properties and exhibited the most potent antioxidation activity from the powdered leaf extract. The extract exhibits prospective promising effects for inducing cell proliferation and apoptosis in breast cancer cells, highlighting its potential for targeted therapeutic interventions. Standardizing methods for Dandelion use is crucial for future clinical applications in cancer treatment. Combining plant-derived compounds with cancer nanotechnology holds the potential for effective strategies in battling malignant diseases. Utilizing liposomes as carriers for phytoconstituent anti-cancer agents offers improved solubility, bioavailability, immunoregulatory effects, advancing anticancer immune function, and reducing toxicity. This integrated approach of natural products and nanotechnology has significant potential to revolutionize healthcare globally, especially in underserved communities where herbal medicine is prevalent.

Keywords: apoptosis, antioxidant activity, cancer nanotechnology, phytopharmaceutical

Procedia PDF Downloads 25

Authors: Xin-Hua Zhao

Abstract:

In recent years, the growth rate of social responsibility reports disclosed by Chinese corporations has grown rapidly. The economic effects of the growing corporate social responsibility reports have become a hot topic. The article takes the chemical listed engineering corporations that disclose social responsibility reports in China as a sample, and based on the information asymmetry theory, examines the economic effect generated by corporate social responsibility disclosure with the method of ordinary least squares. The research is conducted from the perspective of analysts’ earnings forecasts and studies the impact of corporate social responsibility information disclosure on improving the accuracy of analysts' earnings forecasts. The results show that there is a statistically significant negative correlation between corporate social responsibility disclosure index and analysts’ earnings forecast error. The conclusions confirm that enterprises can reduce the asymmetry of social and environmental information by disclosing social responsibility reports, and thus improve the accuracy of analysts’ earnings forecasts. It can promote the effective allocation of resources in the market.

Keywords: analysts' earnings forecasts, corporate social responsibility disclosure, economic effect, information asymmetry

Procedia PDF Downloads 124

Authors: Cho Achidi

Abstract:

Tomato is a crucial food crop significantly contributes to global food and nutrition security. However, postharvest losses severely limit its role. Therefore, it is necessary to develop sustainable strategies to minimize these losses and improve the shelf-life of tomato fruits. One of the major concerns is bacterial infections, particularly by faecal coliform bacteria, which can cause food poisoning and illnesses like diarrhoea and dysentery. This study seeks to identify the presence of coliform bacteria on tomato fruits in fields and markets in Muea, Buea Municipality. The study also evaluated different management strategies to reduce the bacterial incidence and load on tomato fruits. A total of 200 fruits were sampled for both the coliform survey and shelf-life analysis. Ten farmers and traders provided samples, including asymptomatic and symptomatic tomato fruits. The samples designated for shelf-life analysis were treated with Aquatab, warm water, lemon, and onion. The results indicated that out of the 80 symptomatic samples collected, 12.5% contained faecal and total coliform species. Among the ten farms sampled, 14% were infected with coliform bacteria, with the highest infestation rate of 60% recorded in field 4. Furthermore, 15% of the asymptomatic tomato fruits were found to be infected by coliform bacteria. Regarding the management strategies, Aquatabs exhibited the highest efficacy in reducing the incidence of coliform bacteria on tomato fruits, followed by onion and lemon extracts. Although hot water treatment effectively removed bacteria from the fruits, damaging the cell wall negatively affected their shelf-life. Overall, this study emphasizes the severity of coliform bacterial pathogens in the Muea area, particularly their occurrence on asymptomatic tomatoes, which poses a significant concern for plant quarantine services. It also demonstrates potential options for mitigating this bacterial challenge.

Keywords: tomato, shelf-life analysis, food and nutrition security, coliform bbacteria

Procedia PDF Downloads 39

Authors: S. Neelima, P. S. Subramanyam

Abstract:

The major tool for distribution planning is load forecasting, which is the anticipation of the load in advance. Artificial neural networks have found wide applications in load forecasting to obtain an efficient strategy for planning and management. In this paper, the application of neural networks to study the design of short term load forecasting (STLF) Systems was explored. Our work presents a pragmatic methodology for short term load forecasting (STLF) using proposed two-stage model of wavelet transform (WT) and artificial neural network (ANN). It is a two-stage prediction system which involves wavelet decomposition of input data at the first stage and the decomposed data with another input is trained using a separate neural network to forecast the load. The forecasted load is obtained by reconstruction of the decomposed data. The hybrid model has been trained and validated using load data from Telangana State Electricity Board.

Keywords: electrical distribution systems, wavelet transform (WT), short term load forecasting (STLF), artificial neural network (ANN)

Procedia PDF Downloads 398

Authors: Oscar Javier Herrera, Manuel Angel Camacho

Abstract:

This paper addresses a cutting edge method of business demand forecasting, based on an empirical probability function when the historical behavior of the data is random. Additionally, it presents error determination based on the numerical method technique ‘propagation of errors’. The methodology was conducted characterization and process diagnostics demand planning as part of the production management, then new ways to predict its value through techniques of probability and to calculate their mistake investigated, it was tools used numerical methods. All this based on the behavior of the data. This analysis was determined considering the specific business circumstances of a company in the sector of communications, located in the city of Bogota, Colombia. In conclusion, using this application it was possible to obtain the adequate stock of the products required by the company to provide its services, helping the company reduce its service time, increase the client satisfaction rate, reduce stock which has not been in rotation for a long time, code its inventory, and plan reorder points for the replenishment of stock.

Keywords: demand forecasting, empirical distribution, propagation of error, Bogota

Procedia PDF Downloads 591

Authors: Piyachat Chuysinuan, Nitirat Chimnoi, Arthit Makarasen, Nanthawan Reuk-Ngam, Pitt Supaphol, Supanna Techasakul

Abstract:

In this work, biomaterial wound dressings was developed based on gelatin containing herbal substances (essential oil), a substance from the plant Eupatorium adenophorum Spreng (Crofton weed) that used as traditional wound healers. Gelatin hydrogel was prepared from a 10 wt-% gelatin solution. The oil in water (o/w) emulsion Eupatorium adenophorum of essential oil were prepared and used Pluronic F68 as a surfactant. The 10, 20, and 30 % v/v emulsion were mixed with gelatin solution and cast into film. These hydrogels were tested for their gel fraction, swelling and weight loss behavior. With an increase in the emulsion concentration the emulsion-loaded in hydrogels, the gel fraction were decreased due to the crosslink density, while the swelling and weight loss behavior were increased with an increasing in the emulsion content. The potential to use the emulsion-containing gelatin hydrogels as wound dressing was assessed on investigation the release characteristics of the as-loaded hydrogels. The E. adenophorum essential oil was first identified the chemical composition by using GC-MS analysis. The principal components of the oil were p-cymene (16.23%), bornyl acetate (11.84%), and amorpha-4, 7(11)-diene (10.51%). The hydrogel wound dressing containing essential oil was then characterized for their antibacterial activity against Gram-positive and Gram-negative in order to elucidate their potential for use as antibacterial wound dressings by using agar disk diffusion methods. The result showed that E. adenophorum essential oil and the emulsion-loaded gelatin hydrogel inhibited the growth of the test pathogens, Staphylococcus aureus and Staphylococcus epidermidis and increased with increasing the initial amount of essential oil in the hydrogels which confirmed their application as antibacterial wound dressings. Furthermore, the potential use of these wound dressings was further assessed in terms of the indirect cytotoxicity, in vitro attachment and proliferation of dermal human fibroblasts cultured in the hydrogel wound dressings.

Keywords: hydrogel, antibacterial wound dressing, Eupatorium adenophorum essential oil, gelatin

Procedia PDF Downloads 317

Authors: Gajaanuja Megalathan, Banuka Athuraliya

Abstract:

Crime is one of our society's most intimidating and threatening challenges. With the majority of the population residing in cities, many experts and data provided by local authorities suggest a rapid increase in the number of crimes committed in these cities in recent years. There has been an increasing graph in the crime rates. People living in Sri Lanka have the right to know the exact crime rates and the crime rates in the future of the place they are living in. Due to the current economic crisis, crime rates have spiked. There have been so many thefts and murders recorded within the last 6-10 months. Although there are many sources to find out, there is no solid way of searching and finding out the safety of the place. Due to all these reasons, there is a need for the public to feel safe when they are introduced to new places. Through this research, the author aims to develop a mobile application that will be a solution to this problem. It is mainly targeted at tourists, and people who recently relocated will gain advantage of this application. Moreover, the Arima Model combined with ANN is to be used to predict crime rates. From the past researchers' works, it is evidently clear that they haven’t used the Arima model combined with Artificial Neural Networks to forecast crimes.

Keywords: arima model, ANN, crime prediction, data analysis

Procedia PDF Downloads 81

Authors: Muhammad Bilal, Adil Ahmed

Abstract:

This study proposes an approach for electrical induction motor predictive maintenance utilizing machine learning algorithms. On the basis of a study of temperature data obtained from sensors put on the motor, the goal is to predict motor failures. The proposed models are trained to identify whether a motor is defective or not by utilizing machine learning algorithms like Support Vector Machines (SVM) and K-Nearest Neighbors (KNN). According to a thorough study of the literature, earlier research has used motor current signature analysis (MCSA) and vibration data to forecast motor failures. The temperature signal methodology, which has clear advantages over the conventional MCSA and vibration analysis methods in terms of cost-effectiveness, is the main subject of this research. The acquired results emphasize the applicability and effectiveness of the temperature-based predictive maintenance strategy by demonstrating the successful categorization of defective motors using the suggested machine learning models.

Keywords: predictive maintenance, electrical induction motors, machine learning, temperature signal methodology, motor failures

Procedia PDF Downloads 69

Authors: Bibhuti Bhusan Sahoo, Ramakar Jha

Abstract:

Mapping of the extreme events (droughts) is one of the adaptation strategies to consequences of increasing climatic inconsistency and climate alterations. There is no operational practice to forecast the drought. One of the suggestions is to update mapping of drought prone areas for developmental planning. Drought indices play a significant role in drought mitigation. Many scientists have worked on different statistical analysis in drought and other climatological hazards. Many researchers have studied droughts individually for different sub-divisions or for India. Very few workers have studied district wise probabilities over large scale. In the present study, district wise drought probabilities over KBK (Kalahandi-Balangir-Koraput) districts of Odisha, India, Which are seriously prone to droughts, has been established using Hydrological drought index and Meteorological drought index along with the remote sensing drought indices to develop a multidirectional approach in the field of drought mitigation. Mapping for moderate and severe drought probabilities for KBK districts has been done and regions belonging different class intervals of probabilities of drought have been demarcated. Such type of information would be a good tool for planning purposes, for input in modelling and better promising results can be achieved.

Keywords: drought indices, KBK districts, proposed drought severity index, SPI

Procedia PDF Downloads 421

Authors: Ahmed El-Banbi

Abstract:

Several PVT models exist to represent how PVT properties are handled in sub-surface and surface engineering calculations for oil and gas production. The most commonly used models include black oil, modified black oil (MBO), and compositional models. These models are used in calculations that allow engineers to optimize and forecast well and reservoir performance (e.g., reservoir simulation calculations, material balance, nodal analysis, surface facilities, etc.). The choice of which model is dependent on fluid type and the production process (e.g., depletion, water injection, gas injection, etc.). Based on close to 2,000 reservoir fluid samples collected from different basins and locations, this paper presents some conclusions on the occurrence of reservoir fluids. It also reviews the common methods used to classify reservoir fluid types. Based on new criteria related to the production behavior of different fluids and economic considerations, an updated classification of reservoir fluid types is presented in the paper. Recommendations on the use of different PVT models to simulate the behavior of different reservoir fluid types are discussed. Each PVT model requirement is highlighted. Available methods for the calculation of PVT properties from each model are also discussed. Practical recommendations and tips on how to control the calculations to achieve the most accurate results are given.

Keywords: PVT models, fluid types, PVT properties, fluids classification

Procedia PDF Downloads 43

Authors: Georg Brunnthaller, Sandra Stein, Wilfried Sihn

Abstract:

Logistics service providers are facing increasing volatility concerning future transport demand. Short-term planning horizons and planning uncertainties lead to reduced capacity utilisation and increasing empty mileage. To overcome these challenges, a model is proposed to continuously pre-plan future transport capacity in order to redesign and adjust the intermodal fleet accordingly. It is expected that the model will enable logistics service providers to organise more economically and ecologically sustainable transport chains in a more flexible way. To further describe such planning aspects, this paper gives a structured literature review on transport planning problems. The focus is on strategic and tactical planning levels, comprising relevant fleet-sizing-, network-design- and choice-of-carriers-problems. Models and their developed solution techniques are presented and the literature review is concluded with an outlook to our future research objectives

Keywords: choice of transport mode, fleet-sizing, freight transport planning, multimodal, review, service network design

Procedia PDF Downloads 337

Authors: Moses Emetere, Marvel Akinyemi, S. E. Sanni

Abstract:

At the moment, AERONET or AMMA database shows a large volume of data loss. With only about 47% data set available to the scientist, it is evident that accurate nowcast or forecast cannot be guaranteed. The calibration constants of most radiosonde or weather stations are not compatible with the atmospheric conditions of the West African climate. A dispersion model was developed to incorporate salient mathematical representations like a Unified number. The Unified number was derived to describe the turbulence of the aerosols transport in the frictional layer of the lower atmosphere. Fourteen years data set from Multi-angle Imaging SpectroRadiometer (MISR) was tested using the dispersion model. A yearly estimation of the atmospheric constants over Ouagadougou using the model was obtained with about 87.5% accuracy. It further revealed that the average atmospheric constant for Ouagadougou-Niger is a_1 = 0.626, a_2 = 0.7999 and the tuning constants is n_1 = 0.09835 and n_2 = 0.266. Also, the yearly atmospheric constants affirmed the lower atmosphere of Ouagadougou is very dynamic. Hence, it is recommended that radiosonde and weather station manufacturers should constantly review the atmospheric constant over a geographical location to enable about eighty percent data retrieval.

Keywords: aerosols retention, aerosols loading, statistics, analytical technique

Procedia PDF Downloads 278

Authors: Georg Brunnthaller, Sandra Stein, Wilfried Sihn

Abstract:

Transportation service providers are facing increasing volatility concerning future transport demand. Short-term planning horizons and planning uncertainties lead to reduced capacity utilization and increasing empty mileage. To overcome these challenges, a model is proposed to continuously pre-plan future transportation capacity in order to redesign and adjust the intermodal fleet accordingly. It is expected that the model will enable logistics service providers to organize more economically and ecologically sustainable transport chains in a more flexible way. To further describe these planning aspects, this paper gives an overview on transportation planning problems in a structured way. The focus is on strategic and tactical planning levels, comprising relevant fleet-sizing, service-network-design and choice-of-carriers-problems. Models and their developed solution techniques are presented, and the literature review is concluded with an outlook to our future research directions.

Keywords: freight transportation planning, multimodal, fleet-sizing, service network design, choice of transportation mode, review

Procedia PDF Downloads 289

Authors: Maha Hammami, Olfa Benouda Sioud

Abstract:

This study investigates the reliability of management earnings forecasts with reference to these two ingredients: verifiability and neutrality. Specifically, we examine the biasedness (or accuracy) of management earnings forecasts and company specific characteristics that can be associated with accuracy. Based on sample of 102 IPO prospectuses published for admission on NYSE Euronext Paris from 2002 to 2010, we found that these forecasts are on average optimistic and two of the five test variables, earnings variability and financial leverage are significant in explaining ex post bias. Acknowledging the possibility that the bias is the result of the managers’ forecasting behavior, we then examine whether managers decide to under-predict, over-predict or forecast accurately for self-serving purposes. Explicitly, we examine the role of financial distress, operating performance, ownership by insiders and the economy state in influencing managers’ forecasting preferences. We find that managers of distressed firms seem to over-predict future earnings. We also find that when managers are given more stock options, they tend to under-predict future earnings. Finally, we conclude that the management earnings forecasts are affected by an intentional bias due to managers’ forecasting preferences.

Keywords: intentional bias, management earnings forecasts, neutrality, verifiability

Procedia PDF Downloads 204

Authors: Abdulaziz Afandi

Abstract:

Minimizing the inventory cost, optimizing the inventory quantities, and increasing system operational availability are the main motivations to enhance forecasting demand of spare parts in a major power utility company in Medina. This paper reports in an effort made to optimize the orders quantities of spare parts by improving the method of forecasting the demand. The study focuses on equipment that has frequent spare parts purchase orders with uncertain demand. The pattern of the demand considers a lumpy pattern which makes conventional forecasting methods less effective. A comparison was made by benchmarking various methods of forecasting based on experts’ criteria to select the most suitable method for the case study. Three actual data sets were used to make the forecast in this case study. Two neural networks (NN) approaches were utilized and compared, namely long short-term memory (LSTM) and multilayer perceptron (MLP). The results as expected, showed that the NN models gave better results than traditional forecasting method (judgmental method). In addition, the LSTM model had a higher predictive accuracy than the MLP model.

Keywords: neural network, LSTM, MLP, forecasting demand, inventory management

Procedia PDF Downloads 100

Authors: Şakir Bingöl, Abdullah Emre Aydemir, Abdullah Saado, Ahmet Akıl, Elif Canbaz, Feyza Nur Bulgurcu, Gizem Uzun, Günsu Bilge Dal, Muhammedcan Pirinççi

Abstract:

Smart contracts, which can be applied in many different areas, from financial applications to the internet of things, come to the fore with their security, low cost, and self-executing features. In this paper, it is focused on peer-to-peer (P2P) energy trading and the implementation of the smart contract on the Ethereum blockchain. It is assumed a microgrid consists of consumers and prosumers that can produce solar and wind energy. The proposed architecture is a system where the prosumer makes the purchase or sale request in the smart contract and the maximum price obtained through the distribution system operator (DSO) by forecasting. It is aimed to forecast the hourly maximum unit price of energy by using deep learning instead of a fixed pricing. In this way, it will make the system more reliable as there will be more dynamic and accurate pricing. For this purpose, Istanbul's energy generation, energy consumption and market clearing price data were used. The consistency of the available data and forecasting results is observed and discussed with graphs.

Keywords: energy trading smart contract, deep learning, microgrid, forecasting, Ethereum, peer to peer

Procedia PDF Downloads 92

Authors: Antonello Troncone, Luigi Pugliese, Andrea Parise, Enrico Conte

Abstract:

The present study deals with the landslides periodically reactivated by groundwater level fluctuations owing to rainfall. The main type of movement which generally characterizes these landslides consists in sliding with quite small-displacement rates. Another peculiar characteristic of these landslides is that soil deformations are essentially concentrated within a thin shear band located below the body of the landslide, which, consequently, undergoes an approximately rigid sliding. In this context, a simple method is proposed in the present study to forecast the movements of this type of landslides owing to rainfall. To this purpose, the landslide body is schematized by means of a two-block model. Some analytical solutions are derived to relate rainfall measurements with groundwater level oscillations and these latter, in turn, to landslide mobility. The proposed method is attractive for engineering applications since it requires few parameters as input data, many of which can be obtained from conventional geotechnical tests. To demonstrate the predictive capability of the proposed method, the application to a well-documented landslide periodically reactivated by rainfall is shown.

Keywords: rainfall, water level fluctuations, landslide mobility, two-block model

Procedia PDF Downloads 101

Authors: Yusuf Hussaini Atulukwu, Daramola Japheth, Tabitit S. Tabiti, Daramola Elizabeth Lara

Abstract:

In view of the recent limitations faced by the township concerning poorly constructed and in some cases non - existence of drainage facilities that resulted into incessant flooding in some parts of the community poses threat to life,property and the environment. The research seeks to address this issue by showing the spatial distribution of drainage network in Bida Urban using Geographic information System techniques. Relevant features were extracted from existing Bida based Map using un-screen digitization and x, y, z, data of existing drainages were acquired using handheld Global Positioning System (GPS). These data were uploaded into ArcGIS 9.2, software, and stored in the relational database structure that was used to produce the spatial data drainage network of the township. The result revealed that about 40 % of the drainages are blocked with sand and refuse, 35 % water-logged as a result of building across erosion channels and dilapidated bridges as a result of lack of drainage along major roads. The study thus concluded that drainage network systems in Bida community are not in good working condition and urgent measures must be initiated in order to avoid future disasters especially with the raining season setting in. Based on the above findings, the study therefore recommends that people within the locality should avoid dumping municipal waste within the drainage path while sand blocked or weed blocked drains should be clear by the authority concerned. In the same vein the authority should ensured that contract of drainage construction be awarded to professionals and all the natural drainages caused by erosion should be addressed to avoid future disasters.

Keywords: drainage network, spatial, digitization, relational database, waste

Procedia PDF Downloads 310

Authors: Fatemeh Sadat Sharifi

Abstract:

In the present study, the capabilities of WAVEWATCH-III and MIKE21-SW for predicting the characteristics of wind waves in Hormuz Strait are evaluated. The GFS wind data (Global Forecast System) were derived. The bathymetry of gride with 2 arc-minute resolution, also were extracted from the ETOPO1. WAVEWATCH-III findings illustrate more valid prediction of wave features comparing to the MIKE-21 SW in deep water. Apparently, in shallow area, the MIKE-21 provides more uniformities with altimetry measurements. This may be due to the merits of the unstructured grid which are used in MIKE-21, leading to better representations of the coastal area. The findings on the direction of waves generated by wind in the modeling area indicate that in some regions, despite the increase in wind speed, significant wave height stays nearly unchanged. This is fundamental because of swift changes in wind track over the Strait of Hormuz. After discussing wind-induced waves in the region, the impact of instability of the surface layer on wave growth has been considered. For this purpose, the average monthly mean air temperature has been used. The results in cold months, when the surface layer is unstable, indicates an acceptable increase in the accuracy of prediction of the indicator wave height.

Keywords: numerical modeling, WAVEWATCH-III, Strait of Hormuz, MIKE21-SW

Procedia PDF Downloads 181

Authors: Laaziz El Hassan

Abstract:

The Service Network Design Problem (SNDP) is a tactical issue in freight transportation firms. The existing formulations of the problem for intermodal rail-road transportation were not always adapted to the intermodality in terms of full asset utilization and modal shift reinforcement. The objective of the article is to propose a model having a more compliant formulation with intermodality, including constraints highlighting the imperatives of asset management, reinforcing modal shift from road to rail and reducing, by the way, road mode CO2 emissions. The model is a fixed charged, path based integer nonlinear program. Its objective is to minimize services total cost while ensuring full assets utilization to satisfy freight demand forecast. The model's main feature is that it gives as output both the train sizes and the services frequencies for a planning period. We solved the program using a commercial solver and discussed the numerical results.

Keywords: intermodal transport network, service network design, model, nonlinear integer program, path-based, service frequencies, modal shift

Procedia PDF Downloads 95

Authors: Charles Iledun Oyewole, Harira Shuaib

Abstract:

While fulfilling the food security of an increasing population like Nigeria remains a major global concern, more than one-third of crop harvested is lost or wasted during harvesting or in postharvest operations. Reducing the harvest and postharvest losses, especially in developing countries, could be a sustainable solution to increase food availability, eliminate hunger and improve farmers’ livelihoods. Nigeria is one of the countries in sub-Saharan Africa with insufficient food production and high food import bill, which has had debilitating effects on the country’s economy. One of the goals of Nigeria’s agricultural development policy is to ensure that, the nation produces enough food and be less dependent on importation so as to ensure adequate and affordable food for all. Maize could fill the food gap in Nigeria’s effort to beat hunger and food insecurity. Maize is the most important cereal after rice and its production contributes immensely to food availability on the tables of many Nigerians. Maize grains constitute primary source of food for large percentage of the Nigerian populace, thus a considerable waste of this valuable food pre and post-harvest constitutes such a major agricultural bottleneck; that the reduction of pre and post-harvest losses is now a common food security strategy. In surveys conducted, as much as 60% maize outputs can be lost on the field and during the storage stage due to technical inefficiency. Field losses due to rodent damage alone can account for between 10% - 60% grain losses depending on the location. While the use of scientific storage methods can reduce losses below 2% in storage, timely harvesting of crop can check losses on the fields resulting from rodent damage or pest infestation. A push for increased crop production must be complemented by available and affordable post-harvest technologies that will reduce losses on farmers’ fields as well as in storage.

Keywords: government policy, maize, population increase, storage, sustainable food production, yield, yield losses

Procedia PDF Downloads 115

Authors: Benmessaoud Hassen

Abstract:

We propose in this study to address the impact of socio-economic and environmental impact on the physical environment, especially their spatiotemporal dynamics in semi-arid and arid eastern Aurès. Including 11 municipalities, the study area spreads out over a relatively large surface area of about 60.000 ha. The hindsight is quite important and is determined by 03 days of analysis of environmental variation spread over thirty years (between 1987 and 2007). The multi-source data acquired in this context are integrated into a geographic information system (GIS).This allows, among other indices to calculate areas and classes for each thematic layer of the 4 layers previously defined by a method inspired MEDALUS (Mediterranean Desertification and Land Use).The database created is composed of four layers of information (population, livestock, farming and land use). His analysis in space and time has been supplemented by a validation of the ground truth. Once the database has corrected it used to develop the comprehensive map with the calculation of the index of socio-economic and environmental (ISCE). The map supports and the resulting information does not consist only of figures on the present situation but could be used to forecast future trends.

Keywords: impact of socio-economic and environmental, spatiotemporal dynamics, semi-arid environments, GIS, Eastern Aurès

Procedia PDF Downloads 291

Authors: Ludovic Favre, Thibaut M. Schafer, Jean-Luc Robyr, Elena-Lavinia Niederhäuser

Abstract:

This paper presents an optimization method based on genetic algorithm for the energy management inside buildings developed in the frame of the project Smart Living Lab (SLL) in Fribourg (Switzerland). This algorithm optimizes the interaction between renewable energy production, storage systems and energy consumers. In comparison with standard algorithms, the innovative aspect of this project is the extension of the smart regulation over three simultaneous criteria: the energy self-consumption, the decrease of greenhouse gas emissions and operating costs. The genetic algorithm approach was chosen due to the large quantity of optimization variables and the non-linearity of the optimization function. The optimization process includes also real time data of the building as well as weather forecast and users habits. This information is used by a physical model of the building energy resources to predict the future energy production and needs, to select the best energetic strategy, to combine production or storage of energy in order to guarantee the demand of electrical and thermal energy. The principle of operation of the algorithm as well as typical output example of the algorithm is presented.

Keywords: building's energy, control system, energy management, energy storage, genetic optimization algorithm, greenhouse gases, modelling, renewable energy

Procedia PDF Downloads 230

Authors: Djeddi Khaled, Houssou Hind, Miloudi Abdelatif, Rabah Siham

Abstract:

Camels play a vital role as multipurpose animals, providing milk meat and serving as a means of transportation. They serve as a financial reserve for pastoralists and hold significant cultural and social value. Camel milk, known for its exceptional nutritional properties, is considered a valuable substitute for human milk. However, udder infections, particularly mastitis, pose significant challenges to camel farming. Clinical and subclinical mastitis can lead to substantial economic losses. Mastitis, especially the subclinical form, is a persistent and prevalent condition affecting milk hygiene and quality in dairy camels. This review offers insights into the prevalence and risk factors associated with subclinical mastitis in camels. The prevalence of subclinical mastitis in dairy camels was found to range from 9.28% to 87.78%. Major pathogens responsible for camel mastitis include Staphylococcus aureus, Coagulase-negative Staphylococcus, Streptococcus agalactiae, Streptococcus dysgalactiae, Escherichia coli, Micrococcus spp, Pasteurella haemolytica and Corynebacterium spp. The study outlines key risk factors contributing to camel mastitis, emphasizing factors such as severe tick infestation, age, stage of lactation, parity, body condition score, skin lesion on the teats or udders, anti-suckling devices, previous history of the udder, conformation of the udder, breed, unhygienic milking practices, production system, amongst others have been reported to be important in the prevalence of subclinical mastitis. This comprehensive overview provides valuable insights into the multifaceted aspects of camel mastitis, encompassing prevalent bacterial pathogens and diverse risk factors. The findings underscore the importance of holistic management practices, emphasizing hygiene, health monitoring, and targeted interventions to ensure the well-being and productivity of camels in various agro-pastoral contexts.

Keywords: bacterial pathogens, camel, mastitis, risk factors

Procedia PDF Downloads 43

Authors: Yi-Yu Huang, Chien-Kuo Wang, Sreerag Chota Veettil, Hang Zhang, Hu Yike

Abstract:

Regarding the pressing issue of reducing energy consumption and carbon footprint of buildings, past research has focused more on analyzing the thermal performance of the extensive terrestrial green roofs with sedum plants in summer. However, the disadvantages of this type of green roof are relatively limited thermal performance, low extreme weather adaptability, relatively higher demands in maintenance, and lower added value in healing landscape. In view of this, this research aims to develop the extensive wetland green roofs with higher thermal performance, high extreme weather adaptability, low demands in maintenance, and high added value in healing landscape, and to measure its thermal performance for buildings in winter. The following factors are considered including the type and mixing formula of growth medium (light weight soil, akadama, creek gravel, pure water) and the type of aquatic plants. The research adopts a four-stage field experiment conducting on the rooftop of a building in a humid subtropical climate. The results found that emergent (Roundleaf rotala), submerged (Ribbon weed), floating-leaved (Water lily) wetland green roofs had similar thermal performance, and superior over wetland green roof without plant, traditional terrestrial green roof (without plant), and pure water green roof (without plant, nighttime only) in terms of overall passive cooling (8.00C) and thermal insulation (4.50C) effects as well as a reduction in heat amplitude (77-85%) in winter in a humid subtropical climate. The thermal performance of the free-floating (Water hyacinth) wetland green roof is inferior to that of the other three types of wetland green roofs, whether in daytime or nighttime.

Keywords: thermal performance, extensive wetland green roof, Aquatic plant, Winter , Humid subtropical climate

Procedia PDF Downloads 142

Authors: Avinash Kumar Ranjan, Akash Anand

Abstract:

The Land Surface Temperature (LST) is an essential factor accompanying to rise urban heat and climate warming within a city in micro level. It is also playing crucial role in global change study as well as radiation budgets measuring in heat balance studies. The information of LST is very substantial to recognize the urban climatology, ecological changes, anthropological and environmental interactions etc. The Chief motivation of present study focus on time series of ANN model that taken a sequence of LST values of 2000, 2008 and 2016, realize the pattern of variation within the data set and predict the LST values for 2024 and 2032. The novelty of this study centers on evaluation of LST using series of multi-temporal MODIS (MOD 11A2) satellite data by Maximum Value Composite (MVC) techniques. The results derived from this study endorse the proficiency of Geoinformatics Technology with integration of ANN to gain knowledge, understanding and building of precise forecast from the complex physical world database. This study will also focus on influence of Land Use/ Land Cover (LU/LC) variation on Land Surface Temperature.

Keywords: LST, geoinformatics technology, ANN, MODIS satellite imagery, MVC

Procedia PDF Downloads 215

Authors: Cem Isik Dogru, Salih Tekin, Kursad Derinkuyu

Abstract:

Electric vehicle (EV) carsharing is an alternative method to tackle urban transportation problems. This method can be applied by several options. One of the options is the one-way carsharing, which allow an EV to be taken at a designated location and leaving it on another specified location customer desires. Although it may increase users’ satisfaction, the issues, namely, demand dissatisfaction, relocation of EVs and charging schedules, must be dealt with. Also, excessive electricity has to be stored in batteries of EVs. To cope with aforementioned issues, two-step mixed integer programming (MIP) model is proposed. In first step, the integer programming model is used to determine amount of electricity to be sold to the grid in terms of time periods for extra profit. Determined amounts are provided from the batteries of EVs. Also, this step works in day-ahead electricity markets with forecast of periodical electricity prices. In second step, other MIP model optimizes daily operations of one-way carsharing: charging-discharging schedules, relocation of EVs to serve more demand and renting to maximize the profit of EV fleet owner. Due to complexity of the models, heuristic methods are introduced to attain a feasible solution and different price information scenarios are compared.

Keywords: electric vehicles, forecasting, mixed integer programming, one-way carsharing

Procedia PDF Downloads 101