Search results for: demand estimation

Authors: Sudha Summarwar, Sudesh Agarwal, Deepali Lall, Nalini Kataria, Jyotsana Pandey

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Assessment of antioxidant status is an effective tool to appraise the presence of oxidative stress. A combination of assays can be used to evaluate the antioxidant status like serum catalase (CAT), superoxide dismutase (SOD) and monoamine oxidase (MAO). In human medicine pregnancy is known to be associated with oxidative stress. Oxidative stress produces harmful effects to the developing foetus. Several metabolic changes occur in the maternal body to meet the demand of energy of developing foetus. Due to these changes susceptibility of maternal body increases to oxidative stress. There is paucity of research work on this aspect in nondescript goats. Therefore, the present study was intended to appraise the oxidative stress in pregnant and non-pregnant non-descript goat. Blood samples were collected for serum separation in otherwise healthy pregnant and non-pregnant nondescript goats. Mean values of serum CAT, SOD and MAO were found on a higher side (p≤0.05) with serum SOD values showing a rise of 2.5 times higher than the control healthy value. Correlations among all the three parameters were found to be highly significant (p≤0.01) especially greatest in youngest group of pregnant animals. Illustration of result enlightened the veracity of bumped up production of free radicals in pregnant animals. Technical savoir-faire of oxidative stress supervision is essential for upholding of health status of foetus. The upshot of present study undoubtedly implied the development of oxidative stress in pregnant goats on the basis of altered antioxidant status. These findings conclude that initially the oxidative stress due to pregnancy is critically combated by the intricate defensive mechanism of natural antioxidant system of the body. It appears that this imbalance between oxidant and antioxidant must be checked in time to prevent cellular damage by regularly appraising the antioxidant status through laboratory methods.

Keywords: antioxidant, oxidative stress, pregnancy, serum catalase

Procedia PDF Downloads 328

Authors: Soheila Sadeghi

Abstract:

Construction projects are complex and often subject to significant cost overruns due to the multifaceted nature of the activities involved. Accurate cost estimation is crucial for effective budget planning and resource allocation. Traditional methods for predicting overruns often rely on expert judgment or analysis of historical data, which can be time-consuming, subjective, and may fail to consider important factors. However, with the increasing availability of data from construction projects, machine learning techniques can be leveraged to improve the accuracy of overrun predictions. This study applied machine learning algorithms to enhance the prediction of cost overruns in a case study of a construction project. The methodology involved the development and evaluation of two machine learning models: Random Forest and Neural Networks. Random Forest can handle high-dimensional data, capture complex relationships, and provide feature importance estimates. Neural Networks, particularly Deep Neural Networks (DNNs), are capable of automatically learning and modeling complex, non-linear relationships between input features and the target variable. These models can adapt to new data, reduce human bias, and uncover hidden patterns in the dataset. The findings of this study demonstrate that both Random Forest and Neural Networks can significantly improve the accuracy of cost overrun predictions compared to traditional methods. The Random Forest model also identified key cost drivers and risk factors, such as changes in the scope of work and delays in material delivery, which can inform better project risk management. However, the study acknowledges several limitations. First, the findings are based on a single construction project, which may limit the generalizability of the results to other projects or contexts. Second, the dataset, although comprehensive, may not capture all relevant factors influencing cost overruns, such as external economic conditions or political factors. Third, the study focuses primarily on cost overruns, while schedule overruns are not explicitly addressed. Future research should explore the application of machine learning techniques to a broader range of projects, incorporate additional data sources, and investigate the prediction of both cost and schedule overruns simultaneously.

Keywords: cost prediction, machine learning, project management, random forest, neural networks

Procedia PDF Downloads 35

Authors: Muhammad Waheed Mushtaq, Shahid bashir, Atta Ur Rehman

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In the past decade, the growing demand for capital and the increased utilization of supercapacitors reflect advancements in energy-producing systems and energy storage devices. Metal oxides and ferrites have emerged as promising candidates for supercapacitors and batteries. In our current study, we synthesized Lithium manganese nanoferrite, denoted as LixMnxFe₂O₄, using the hydrothermal technique. Subsequently, we treated it with sodium dodecyl benzene sulphonate (SDBS) surfactant to create nanocomposites of Lithium manganese nano ferrite (LMFe) with poly pyrrole (LixMnxFe₂O₄-PPY). We employed Powder X-ray diffraction (XRD) to confirm the crystalline nature and spinel phase structure of LMFe nanoparticles, which exhibited a single-phase crystal structure, indicating sample purity. To assess the surface topography, morphology, and grain size of both synthesized LixMnxFe₂O₄ and LixMnxFe₂O₄-PPY, we used atomic force microscopy and scanning electron microscopy (SEM). The average particle size of pure ferrite was found to be 54 nm, while that of its nanocomposite was 71 nm. Energy dispersive X-ray (EDX) analysis confirmed the presence of all required elements, including Li, Mn, Fe, and O, in the appropriate proportions. Saturation magnetization (32.69 emu), remanence (Mr), and coercive force (Hc) were measured using a Vibrating Sample Magnetometer (VSM). To assess the electrochemical performance of the material, we conducted Cyclic Voltammetry (CV) measurements for both pure LMFe and LMFe-PPY. The CV results for LMFe-PPY demonstrated that specific capacitance decreased with increasing scan rate while the area of the current-voltage loop increased. These findings are promising for the development of supercapacitors and lithium-ion batteries (LIBs).

Keywords: lithium manganese ferrite, poly pyrrole, nanocomposites, cyclic voltammetry, cathode

Procedia PDF Downloads 60

Authors: Rajkumar Ghosh

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Climate change has led to decreased water availability in the Gomti Nagar area of Uttar Pradesh, India. Climate change has reduced the amount of precipitation and increased the rate of evaporation. The region is heavily reliant on surface water sources (Gomti river, Sharda Canal) and groundwater. Efficient management of groundwater resources is crucial for addressing water shortages. These may include: Exploring alternative water sources, such as wastewater recycling and desalination, can help augment water supply and reduce dependency on rainfall-dependent sources. Promoting the use of water-efficient technologies in industries, agriculture, and water-efficient infrastructure in urban areas can contribute to reducing water demand and optimizing water use. Incorporating climate change considerations into urban planning and infrastructure development can help ensure water security in the face of future climate uncertainties. Addressing water scarcity in the Gomti Nagar area requires a multi-pronged approach that combines sustainable groundwater management practices, climate change adaptation strategies, and integrated water resource management. By implementing these measures, the region can work towards ensuring a more sustainable and reliable water supply in the context of climate change. Water is the most important natural resource for the existence of living beings in the Earth's ecosystem. On Earth, 1.2 percent of the water is drinkable, but only 0.3 percent is usable by people. Water scarcity is a growing concern in India due to the impact of climate change and over-exploitation of water resources. Excess groundwater withdrawal causes regular declines in groundwater level. Due to city boundary expansion and growing urbanization, the recharge point for groundwater tables is decreasing. Rainwater infiltration into the subsoil is also reduced by unplanned, uneven settlements in urban change.

Keywords: climate change, water scarcity, groundwater, rainfall, water supply

Procedia PDF Downloads 73

Authors: Shashank. S. Bagane, H. N. Rajendra Prasad

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Building Information Modeling (BIM) has currently developed into a potent solution. The consistent development of BIM technology in the sphere of Architecture, Engineering, and Construction (AEC) industry has enhanced the effectiveness of construction and decision making. However, aggrandized global warming and energy crisis has impacted on building energy analysis. It is now becoming an important factor to be considered in the AEC industry. Amalgamating energy analysis in the planning and design phase of a structure has become a necessity. In the current construction industry, estimating energy usage and reducing its footprint is of high priority. The construction industry is giving more prominence to sustainability alongside energy efficiency. This demand is compelling the designers, planners, and engineers to inspect the sustainable performance throughout the building's life cycle. The current study primarily focuses on energy consumption, space arrangement, and spatial geometry of a residential building. Most commonly residential structures in India are constructed considering Vastu Shastra. Vastu designs are intended to integrate architecture with nature and utilizing geometric patterns, symmetry, and directional alignments. In the current study, a residential brick masonry structure is considered for BIM analysis, Architectural model of the structure will be created using Revit software, later the orientation and spatial arrangement will be finalized based on Vastu principles. Furthermore, the structure will be investigated for the impact of building orientation and spatial arrangements on energy using Green Building Studio software. Based on the BIM analysis of the structure, energy consumption of subsequent building orientations will be understood. A well-orientated building having good spatial arrangement can save a considerable amount of energy throughout its life cycle and reduces the need for heating and lighting which will prove to diminish energy usage and improve the energy efficiency of the residential building.

Keywords: building information modeling, energy impact, spatial geometry, vastu

Procedia PDF Downloads 153

Authors: Innocensia John, Henrik Egelyng, Razack Lokina

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As the world`s population is constantly growing, food security has become a thorny trending issue. The impact has particularly been felt more in Africa as most of the people depend on food Agriculture products. Geographical Indications can aid in transforming the Tanzania agriculture-dependent economy through tapping the unique attributes of their quality products like soil, taste color etc. Consumers worldwide demand more uniquer products featuring a ´connect´ with the land use systems producing particular qualities. Tanzania has demonstrated the capacity to tap into the organic world market and has untapped potential for harvesting market value from geographical indications. This paper presents preliminary results from VALOR — a research project investigating conditions under which Tanzanian origin food producers can add value by incorporating territory specific cultural, environmental and social qualities into marketing, production and processing of unique local, niche and specialty products. Cases are investigated of the prospects for Tanzania to leapfrog perhaps into exports of geographical indications products, and certainly into allowing smallholders to create employment and build monetary value, while stewarding local food cultures and natural environments and resources, and increasing the diversity of supply of natural and unique quality products and so contribute to enhanced food security. Rice from Kyela, coffee and Sugar from Kilimanjaro, are some of the product cases investigated and provides for the in-depth case study, as ´landscape´ products incorporating ´taste of place´. Framework conditions for producers creating or capturing market value as stewards of cultural and landscape values and environments and institutional requirements for such creation or capturing to happen, including presence of export opportunities, are discussed.

Keywords: food origins, food security, protected geographical indications, case study analysis

Procedia PDF Downloads 293

Authors: Oshma Chakoory, Sophie Comtet-Marre, Pierre Peyret

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Metagenomic classifiers are widely used for the taxonomic profiling of metagenomic data and estimation of taxa relative abundance. Small subunit rRNA genes are nowadays a gold standard for the phylogenetic resolution of complex microbial communities, although the power of this marker comes down to its use as full-length. We benchmarked the performance and accuracy of rRNA-specialized versus general-purpose read mappers, reference-targeted assemblers and taxonomic classifiers. We then built a pipeline called RiboTaxa to generate a highly sensitive and specific metataxonomic approach. Using metagenomics data, RiboTaxa gave the best results compared to other tools (Kraken2, Centrifuge (1), METAXA2 (2), PhyloFlash (3)) with precise taxonomic identification and relative abundance description, giving no false positive detection. Using real datasets from various environments (ocean, soil, human gut) and from different approaches (metagenomics and gene capture by hybridization), RiboTaxa revealed microbial novelties not seen by current bioinformatics analysis opening new biological perspectives in human and environmental health. In a study focused on corals’ health involving 20 metagenomic samples (4), an affiliation of prokaryotes was limited to the family level with Endozoicomonadaceae characterising healthy octocoral tissue. RiboTaxa highlighted 2 species of uncultured Endozoicomonas which were dominant in the healthy tissue. Both species belonged to a genus not yet described, opening new research perspectives on corals’ health. Applied to metagenomics data from a study on human gut and extreme longevity (5), RiboTaxa detected the presence of an uncultured archaeon in semi-supercentenarians (aged 105 to 109 years) highlighting an archaeal genus, not yet described, and 3 uncultured species belonging to the Enorma genus that could be species of interest participating in the longevity process. RiboTaxa is user-friendly, rapid, allowing microbiota structure description from any environment and the results can be easily interpreted. This software is freely available at https://github.com/oschakoory/RiboTaxa under the GNU Affero General Public License 3.0.

Keywords: metagenomics profiling, microbial diversity, SSU rRNA genes, full-length phylogenetic marker

Procedia PDF Downloads 108

Authors: Janne Engblom, Elias Oikarinen

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A panel dataset is one that follows a given sample of individuals over time, and thus provides multiple observations on each individual in the sample. Panel data models include a variety of fixed and random effects models which form a wide range of linear models. A special case of panel data models are dynamic in nature. A complication regarding a dynamic panel data model that includes the lagged dependent variable is endogeneity bias of estimates. Several approaches have been developed to account for this problem. In this paper, the panel models were estimated using the Arellano-Bover/Blundell-Bond Generalized method of moments (GMM) estimator which is an extension of the Arellano-Bond model where past values and different transformations of past values of the potentially problematic independent variable are used as instruments together with other instrumental variables. The Arellano–Bover/Blundell–Bond estimator augments Arellano–Bond by making an additional assumption that first differences of instrument variables are uncorrelated with the fixed effects. This allows the introduction of more instruments and can dramatically improve efficiency. It builds a system of two equations—the original equation and the transformed one—and is also known as system GMM. In this study, Finnish housing price dynamics were examined empirically by using the Arellano–Bover/Blundell–Bond estimation technique together with ordinary OLS. The aim of the analysis was to provide a comparison between conventional fixed-effects panel data models and dynamic panel data models. The Arellano–Bover/Blundell–Bond estimator is suitable for this analysis for a number of reasons: It is a general estimator designed for situations with 1) a linear functional relationship; 2) one left-hand-side variable that is dynamic, depending on its own past realizations; 3) independent variables that are not strictly exogenous, meaning they are correlated with past and possibly current realizations of the error; 4) fixed individual effects; and 5) heteroskedasticity and autocorrelation within individuals but not across them. Based on data of 14 Finnish cities over 1988-2012 differences of short-run housing price dynamics estimates were considerable when different models and instrumenting were used. Especially, the use of different instrumental variables caused variation of model estimates together with their statistical significance. This was particularly clear when comparing estimates of OLS with different dynamic panel data models. Estimates provided by dynamic panel data models were more in line with theory of housing price dynamics.

Keywords: dynamic model, fixed effects, panel data, price dynamics

Procedia PDF Downloads 1477

Authors: Basma Abdel Mohsen Al-Sheikh

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This study aimed to create textile designs inspired by Najdi Al-Sadu art, with the objective of highlighting Saudi identity and heritage. The research proposed clothing designs for women and children, utilizing textiles inspired by Najdi Al-Sadu art, and incorporated artificial intelligence techniques in the design process. The study employed a descriptive-analytical approach to describe Najdi Al-Sadu, and an experimental method involving the creation of textile designs inspired by Al-Sadu. The study sample consisted of 33 participants, including experts in the fashion and textile industry, fashion designers, lecturers, professors, and postgraduate students from King Abdulaziz University. A questionnaire was used as a tool to gather opinions regarding the proposed designs. The results demonstrated a clear acceptance of the designs inspired by Najdi Al-Sadu and incorporating artificial intelligence, with approval rates ranging from 22% to 81% across different designs. The study concluded that artificial intelligence applications have a significant impact on fashion design, particularly in the integration of Al-Sadu art. The findings also indicated a positive reception of the designs in terms of their aesthetic and functional aspects, although individual preferences led to some variations in opinions. The results highlighted a demand for designs that combine heritage and modern fashion, striking a balance between authenticity and contemporary style. The study recommended that designers continue to explore ways to integrate cultural heritage, such as Al-Sadu art, with contemporary design elements to achieve this balance. Furthermore, it emphasized the importance of enhancing the aesthetic and functional aspects of designs, taking into consideration the preferences of the target market and customer expectations. The effective utilization of artificial intelligence was also emphasized to improve design processes, expand creative possibilities, and foster innovation and authenticity.

Keywords: Najdi Al-Sadu art, artificial intelligence, women's and children's fashion, clothing designs

Procedia PDF Downloads 65

Authors: Akhila Potluru

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Worldwide, more than 250 bodies of water are transboundary, meaning they cross the political boundaries of multiple countries. This creates a system of hydrological, economic, and social interdependence between communities reliant on these water sources. Transboundary water conflicts can occur as a result of this intense interdependence. Many factors contribute to the sparking of transboundary water conflicts, ranging from natural hydrological factors to hydro-political interactions. Previous attempts to predict transboundary water conflicts by analysing changes or trends in the contributing factors have typically failed because patterns in the data are hard to identify. However, there is potential for artificial intelligence and machine learning to fill this gap and identify future ‘hotspots’ up to a year in advance by identifying patterns in data where humans can’t. This research determines the extent to which AI can be used to predict and prevent transboundary water conflicts. This is done via a critical literature review of previous case studies and datasets where AI was deployed to predict water conflict. This research not only delivered a more nuanced understanding of previously undervalued factors that contribute toward transboundary water conflicts (in particular, culture and disinformation) but also by detecting conflict early, governance bodies can engage in processes to de-escalate conflict by providing pre-emptive solutions. Looking forward, this gives rise to significant policy implications and water-sharing agreements, which may be able to prevent water conflicts from developing into wide-scale disasters. Additionally, AI can be used to gain a fuller picture of water-based conflicts in areas where security concerns mean it is not possible to have staff on the ground. Therefore, AI enhances not only the depth of our knowledge about transboundary water conflicts but also the breadth of our knowledge. With demand for water constantly growing, competition between countries over shared water will increasingly lead to water conflict. There has never been a more significant time for us to be able to accurately predict and take precautions to prevent global water conflicts.

Keywords: artificial intelligence, machine learning, transboundary water conflict, water management

Procedia PDF Downloads 98

Authors: John Onolame Unuofin, Uchechukuw U. Nwodo, Anthony I. Okoh

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Laccase is constantly gaining status as important biocatalyst in biotechnology. The illimitable potential of its industrial applications and the corresponding aggressive need for phenomenal volumes of extracellularly secreted laccases have called for its interminable production from sources which are able to meet this demand within a relatively short period of time, preferably bacteria. In response to this call, this study was designed to source for laccase-producing bacteria from different environmental matrices. Three sampling environments were chosen such as wastewater treatment plants, University of Fort Hare vicinity and the Hogback woodland, all within the Eastern Cape, South Africa. Samples such as effluents, sediments, leaf litters, degrading wood and rock scrapings were selectively enriched with some model aromatic compounds and were further screened qualitatively and quantitatively on five phenolic substrates ABTS (2,2’-azino-bis(3-ethylbenzothiazoline-6-sulphonic acid), Guaiacol, 1-Naphthol, Potassium Ferric Cyanide and Syringaldazine). Basis for selection was their ability to elicit a colour change on at least three of the above mentioned agar based assay substrates. The choice isolates were further identified based on 16S rRNA molecular identification techniques. 33 isolates were screened out of the 40 representative distinct colonies during the qualitative plate screens, while quantitative screens selected out 11 bacterial isolates. They were, based on molecular identification, desginated as members of the genera Pseudomonas, Stenotrophomonas and Citrobacter of the gammaproteobacteria and Bordetalla and Achromobacter of the betaproteobacteria respectively. We therefore conclude based on our outcomes that we may have isolated efficient laccase-producing bacteria, which might be of beneficial significance in catalysis and biotechnology.

Keywords: beta proteobacteria, catalysis, gammaproteobacteria, laccase

Procedia PDF Downloads 168

Authors: Gheyder Concha Bendezu, Rodrigo Lescano Loli, Aldo Bravo Lizano

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In big urbanized cities of Latin America, motor vehicles have priority over non-motor vehicles and pedestrians. There is an important problem that affects people's health and quality of life; lack of inclusion towards pedestrians makes it difficult for them to move smoothly and safely since the city has been planned for the transit of motor vehicles. Faced with the new trend for sustainable and economical transport, the city is forced to develop infrastructure in order to incorporate pedestrians and users with non-motorized vehicles in the transport system. The present research aims to study the influence of non-motorized vehicles on an avenue, the optimization of a cycle using traffic lights based on simulation in Synchro software, to improve the flow of non-motor vehicles. The evaluation is of the microscopic type; for this reason, field data was collected, such as vehicular, pedestrian, and non-motor vehicle user demand. With the values of speed and travel time, it is represented in the current scenario that contains the existing problem. These data allow to create a microsimulation model in Vissim software, later to be calibrated and validated so that it has a behavior similar to reality. The results of this model are compared with the efficiency parameters of the proposed model; these parameters are the queue length, the travel speed, and mainly the travel times of the users at this intersection. The results reflect a reduction of 27% in travel time, that is, an improvement between the proposed model and the current one for this great avenue. The tail length of motor vehicles is also reduced by 12.5%, a considerable improvement. All this represents an improvement in the level of service and in the quality of life of users.

Keywords: bikeway, microsimulation, pedestrians, queue length, traffic light cycle, travel time

Procedia PDF Downloads 157

Authors: Soheila Sadeghi

Abstract:

Construction projects are complex and often subject to significant cost overruns due to the multifaceted nature of the activities involved. Accurate cost estimation is crucial for effective budget planning and resource allocation. Traditional methods for predicting overruns often rely on expert judgment or analysis of historical data, which can be time-consuming, subjective, and may fail to consider important factors. However, with the increasing availability of data from construction projects, machine learning techniques can be leveraged to improve the accuracy of overrun predictions. This study applied machine learning algorithms to enhance the prediction of cost overruns in a case study of a construction project. The methodology involved the development and evaluation of two machine learning models: Random Forest and Neural Networks. Random Forest can handle high-dimensional data, capture complex relationships, and provide feature importance estimates. Neural Networks, particularly Deep Neural Networks (DNNs), are capable of automatically learning and modeling complex, non-linear relationships between input features and the target variable. These models can adapt to new data, reduce human bias, and uncover hidden patterns in the dataset. The findings of this study demonstrate that both Random Forest and Neural Networks can significantly improve the accuracy of cost overrun predictions compared to traditional methods. The Random Forest model also identified key cost drivers and risk factors, such as changes in the scope of work and delays in material delivery, which can inform better project risk management. However, the study acknowledges several limitations. First, the findings are based on a single construction project, which may limit the generalizability of the results to other projects or contexts. Second, the dataset, although comprehensive, may not capture all relevant factors influencing cost overruns, such as external economic conditions or political factors. Third, the study focuses primarily on cost overruns, while schedule overruns are not explicitly addressed. Future research should explore the application of machine learning techniques to a broader range of projects, incorporate additional data sources, and investigate the prediction of both cost and schedule overruns simultaneously.

Keywords: resource allocation, machine learning, optimization, data-driven decision-making, project management

Procedia PDF Downloads 28

Authors: Vahid Ramezankhani, Keith J. Stevenson, Stanislav. S. Fedotov

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Lithium-ion batteries (LIBs) play a pivotal role in achieving the key objective “zero-carbon emission” as countries agreed to reach a 1.5ᵒC global warming target according to the Paris agreement. Nowadays, due to the tremendous mobile and stationary consumption of small/large-format LIBs, the demand and consequently the price for such energy storage devices have been raised. The aforementioned challenges originate from the shrinkage of the major applied critical materials in these batteries, such as cobalt (Co), nickel (Ni), Lithium (Li), graphite (G), and manganese (Mn). Therefore, it is imperative to consider alternative elements to address issues corresponding to the limitation of resources around the globe. Potassium (K) is considered an effective alternative to Li since K is a more abundant element, has a higher operating potential, a faster diffusion rate, and the lowest stokes radius in comparison to the closest neighbors in the periodic table (Li and Na). Among all reported materials for metal-ion batteries, some of them possess the general formula AMXO4L [A = Li, Na, K; M = Fe, Ti, V; X = P, S, Si; L= O, F, OH] is of potential to be applied both as anode and cathode and enable researchers to investigate them in the full symmetric battery format. KTiPO4F (KTP structural material) has been previously reported by our group as a promising cathode with decent electronic properties. Herein, we report a synthesis, crystal structure characterization, morphology, as well as K-ion storage properties of KTiPO4F. Our investigation reveals that KTiPO4F delivers discharge capacity > 150 mAh/g at 26.6 mA/g (C/5 current rate) in the potential window of 0.001-3 V. Surprisingly, the cycling performance of C-KTiPO4F//K cell is stable for 1000 cycles at 130 mA/g (C current rate), presenting capacity > 130 mAh/g. More interestingly, we achieved to assemble full symmetric batteries where carbon-coated KTiPO4F serves as both negative and positive electrodes, delivering >70 mAh/g in the potential range of 0.001-4.2V.

Keywords: anode material, potassium battery, chemical characterization, electrochemical properties

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Authors: Peter C. St. John, Michael F. Crowley, Yannick J. Bomble

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Production of chemicals from engineered organisms in a batch culture typically involves a trade-off between productivity, yield, and titer. However, strategies for strain design typically involve designing mutations to achieve the highest yield possible while maintaining growth viability. Such approaches tend to follow the principle of designing static networks with minimum metabolic functionality to achieve desired yields. While these methods are computationally tractable, optimum productivity is likely achieved by a dynamic strategy, in which intracellular fluxes change their distribution over time. One can use multi-stage fermentations to increase either productivity or yield. Such strategies would range from simple manipulations (aerobic growth phase, anaerobic production phase), to more complex genetic toggle switches. Additionally, some computational methods can also be developed to aid in optimizing two-stage fermentation systems. One can assume an initial control strategy (i.e., a single reaction target) in maximizing productivity - but it is unclear how close this productivity would come to a global optimum. The calculation of maximum theoretical yield in metabolic engineering can help guide strain and pathway selection for static strain design efforts. Here, we present a method for the calculation of a maximum theoretical productivity of a batch culture system. This method follows the traditional assumptions of dynamic flux balance analysis: that internal metabolite fluxes are governed by a pseudo-steady state and external metabolite fluxes are represented by dynamic system including Michealis-Menten or hill-type regulation. The productivity optimization is achieved via dynamic programming, and accounts explicitly for an arbitrary number of fermentation stages and flux variable changes. We have applied our method to succinate production in two common microbial hosts: E. coli and A. succinogenes. The method can be further extended to calculate the complete productivity versus yield Pareto surface. Our results demonstrate that nearly optimal yields and productivities can indeed be achieved with only two discrete flux stages.

Keywords: A. succinogenes, E. coli, metabolic engineering, metabolite fluxes, multi-stage fermentations, succinate

Procedia PDF Downloads 206

Authors: Stephen Acheampong Amoafoh

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In today's dynamic economic landscape, businesses face multifaceted challenges that demand strategic foresight and informed decision-making. This abstract explores the pivotal role of financial analytics in driving business performance amidst evolving market conditions. By integrating accounting principles with economic insights, organizations can harness the power of data-driven strategies to optimize resource allocation, mitigate risks, and capitalize on emerging opportunities. This presentation will delve into the practical applications of financial analytics across various sectors, highlighting case studies and empirical evidence to underscore its efficacy in enhancing operational efficiency and fostering sustainable growth. From predictive modeling to performance benchmarking, attendees will gain invaluable insights into leveraging advanced analytics tools to drive profitability, streamline processes, and adapt to changing market dynamics. Moreover, this abstract will address the ethical considerations inherent in financial analytics, emphasizing the importance of transparency, integrity, and accountability in data-driven decision-making. By fostering a culture of ethical conduct and responsible stewardship, organizations can build trust with stakeholders and safeguard their long-term viability in an increasingly interconnected global economy. Ultimately, this abstract aims to stimulate dialogue and collaboration among scholars, practitioners, and policymakers, fostering knowledge exchange and innovation in the realms of accounting, business, and economics. Through interdisciplinary insights and actionable recommendations, participants will be equipped to navigate the complexities of today's business environment and seize opportunities for sustainable success.

Keywords: financial analytics, business performance, data-driven strategies, sustainable growth

Procedia PDF Downloads 42

Authors: Nawt Almutairi, Ramzi El-Haddadeh

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With the extensive increase in the number of online shops, loyalty becomes the most purpose for e-retailers by which they can maintain their exit customers and regular income instead of spending large deal of money to target new segmentation. To obtain customers’ loyalty e-marketers should firstly satisfy customers by providing a high quality of services that could fulfil their demand. They have to satisfy them to trust the web-site then increase their intention to re-visit it. This study intends to investigate to what extend the elements of e-service quality presented in the literature affect customers’ satisfaction and how these influences contribute to customers’ trust and loyalty. Three dimensions of service quality are estimated. The first element is web-site interactivity, which is perceived the quality of interactive support and the accessible communications-tool. The second aspect is security/privacy, which is perceived the quality of controlling security and privacy while transaction over the web-site. The third element is web-design that perceived a pleasant user interface with visual appealing. These elements present positive effects on shoppers’ satisfaction. Thus, To examine the proposed constructs of this research, some measurements scale-items adapted from similar prior studies. Survey data collected online from Saudi customers (n=106) were utilized to test the research hypotheses. After that, the hypotheses were analyzed by using a variety of regression tools. The analytical results of this study propose that perceived quality of interactivity and security/privacy affects customers’ satisfaction. As well as trust seems to be a substantial construct that highly affects loyalty in online shopping. This study provides a developed model to obtain a simple understanding of the series of customers’ loyalty in online shopping. One construct presenting in the research model is web-design appears to be not important antecedent of satisfaction (the path to loyalty) in online shopping.

Keywords: e-service, satisfaction, trust, loyalty

Procedia PDF Downloads 252

Authors: George Cudjoe Agbemabiese, John Paul Kosiba, Michael Boadi Nyamekye, Vanessa Narkie Tetteh, Caleb Nunoo, Mohammed Muniru Husseini

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The smartphone industry continues to experience massive growth, evidenced by expanding markets and an increasing number of brands, models and manufacturers. As technology advances rapidly, manufacturers of smartphones are consistently introducing new innovations to keep up with the latest evolving industry trends and customer demand for more modern devices. This study aimed to assess the influence of artificial intelligence (AI) voice assistant (VA) on improving customer experience, resulting in the continuous use of mobile brands. Specifically, this article assesses the role of hedonic, utilitarian, and social benefits provided by AIVA on customer experience and the continuance intention to use mobile phone brands. Using a primary data collection instrument, the quantitative approach was adopted to examine the study's variables. Data from 348 valid responses were used for the analysis based on structural equation modeling (SEM) with AMOS version 23. Three main factors were identified to influence customer experience, which results in continuous usage of mobile phone brands. These factors are social benefits, hedonic benefits, and utilitarian benefits. In conclusion, a significant and positive relationship exists between the factors influencing customer experience for continuous usage of mobile phone brands. The study concludes that mobile brands that invest in delivering positive user experiences are in a better position to improve usage and increase preference for their brands. The study recommends that mobile brands consider and research their prospects' and customers' social, hedonic, and utilitarian needs to provide them with desired products and experiences.

Keywords: artificial intelligence, continuance usage, customer experience, smartphone industry

Procedia PDF Downloads 73

Authors: Jaspreet Kaur, Parminder Nain, Vipin Saini, Sumitra Dahiya

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Elaeocarpus sphaericus has been traditionally used in the Indian traditional medicine system for the treatment of stress, anxiety, depression, palpitation, epilepsy, migraine and lack of concentration. The study was investigated to evaluate the neurological potential such as anxiolytic, muscle relaxant and sedative activity of methanolic extract of Elaeocarpus sphaericus stem bark (MEESSB) in mice. Preliminary phytochemical screening and acute oral toxicity of MEESSB was carried out by using standard methods. The anxiety was induced by employing Elevated Plus-Maze (EPM), Light and Dark Test (LDT), Open Field Test (OFT) and Social Interaction test (SIT). The motor coordination and sedative effect was also observed by using actophotometer, rota-rod apparatus and ketamine-induced sleeping time, respectively. Animals were treated with different doses of MEESSB (i.e.100, 200, 400 and 800 mg/kg orally) and diazepam (2 mg/kg i.p) for 21 days. Brain neurotransmitters like dopamine, serotonin and nor-epinephrine level were estimated by validated methods. Preliminary phytochemical analysis of the extract revealed the presence of tannins, phytosterols, steroids and alkaloids. In the acute toxicity studies, MEESSB was found to be non-toxic and with no mortality. In anxiolytic studies, the different doses of MEESSB showed a significant (p<0.05) effect on EPM and LDT. In OFT and SIT, a significant (p<0.05) increase in ambulation, rearing and social interaction time was observed. In the case of motor coordination activity, the MEESSB does not cause any significant effect on the latency to fall off from the rotarod bar as compared to the control group. Moreover, no significant effects on ketamine-induced sleep latency and total sleeping time induced by ketamine were observed. Results of neurotransmitter estimation revealed the increased concentration of dopamine, whereas the level of serotonin and nor-epinephrine was found to be decreased in the mice brain, with MEESSB at dose 800 mg/kg only. The study has validated the folkloric use of the plant as an anxiolytic in Indian traditional medicine while also suggesting potential usefulness in the treatment of stress and anxiety without causing sedation.

Keywords: anxiolytic, behavior experiments, brain neurotransmitters, elaeocarpus sphaericus

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Authors: M. F. Wu, F. T. Cheng, C. S. Wu, M. C. Tan

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It is a critical time to upgrade technology and increase value added with manufacturing skills developing and management strategies that will highly satisfy the customers need in the precision machinery global market. In recent years, the supply side, each precision machinery manufacturers in each country are facing the pressures of price reducing from the demand side voices that pushes the high-end precision machinery manufacturers adopts low-cost and high-quality strategy to retrieve the market. Because of the trend of the global market, the manufacturers must take price reducing strategies and upgrade technology of low-end machinery for differentiations to consolidate the market. By using six key success factors (KSFs), customer perceived value, customer satisfaction, customer service, product design, product effectiveness and machine structure quality are causal conditions to explore the impact of competitive advantage of the enterprise, such as overall profitability and product pricing power. This research uses key success paths (KSPs) approach and f/s QCA software to explore various combinations of causal relationships, so as to fully understand the performance level of KSFs and business objectives in order to achieve competitive advantage. In this study, the combination of a causal relationships, are called Key Success Paths (KSPs). The key success paths guide the enterprise to achieve the specific outcomes of business. The findings of this study indicate that there are thirteen KSPs to achieve the overall profitability, sixteen KSPs to achieve the product pricing power and seventeen KSPs to achieve both overall profitability and pricing power of the enterprise. The KSPs provide the directions of resources integration and allocation, improve utilization efficiency of limited resources to realize the continuous vision of the enterprise.

Keywords: precision machinery industry, key success factors (KSFs), key success paths (KSPs), overall profitability, product pricing power, competitive advantages

Procedia PDF Downloads 259

Authors: O. E. Akinyemi, P. O. Alege, O. O. Ajayi, L. A. Amaghionyediwe, A. A. Ogundipe

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Nigeria as an oil-producing developing country in Africa is one of the many countries that had been subsidizing consumption of fossil fuel. Despite the numerous advantage of this policy ranging from increased energy access, fostering economic and industrial development, protecting the poor households from oil price shocks, political considerations, among others; they have been found to impose economic cost, wasteful, inefficient, create price distortions discourage investment in the energy sector and contribute to environmental pollution. These negative consequences coupled with the fact that the policy had not been very successful at achieving some of its stated objectives, led to a number of organisations and countries such as the Group of 7 (G7), World Bank, International Monetary Fund (IMF), International Energy Agency (IEA), Organisation for Economic Co-operation and Development (OECD), among others call for global effort towards reforming fossil fuel subsidies. This call became necessary in view of seeking ways to harmonise certain existing policies which may by design hamper current effort at tackling environmental concerns such as climate change. This is in addition to driving a green growth strategy and low carbon development in achieving sustainable development. The energy sector is identified to play a vital role. This study thus investigates the prospects of using fuel subsidy reform as a viable tool in driving an economy that de-emphasizes carbon growth in Nigeria. The method used is the Johansen and Engle-Granger two-step Co-integration procedure in order to investigate the existence or otherwise of a long-run equilibrium relationship for the period 1971 to 2011. Its theoretical framework is rooted in the Environmental Kuznet Curve (EKC) hypothesis. In developing three case scenarios (case of subsidy payment, no subsidy payment and effective subsidy), findings from the study supported evidence of a long run sustainable equilibrium model. Also, estimation results reflected that the first and the second scenario do not significantly influence the indicator of environmental quality. The implication of this is that in reforming fuel subsidy to drive environmental quality for an economy like Nigeria, strong and effective regulatory framework (measure that was interacted with fuel subsidy to yield effective subsidy) is essential.

Keywords: environmental quality, fuel subsidy, green growth, low carbon growth strategy

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Authors: Arvan Prakash Ankitha, Madasamy Arockiasamy

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The increasing demand for adopting pile foundations in bridgeshas pointed towardsthe need to constantly improve the existing analytical techniques for better understanding of the behavior of such foundation systems. This study presents a simplistic approach using the energy-based method to assess the displacement responses of piles subjected to general loading conditions: Axial Load, Lateral Load, and a Bending Moment. The governing differential equations and the boundary conditions for a bridge pile embedded in multi-layered soil strata subjected to the general loading conditions are obtained using the Hamilton’s principle employing variational principles and minimization of energies. The soil non-linearity has been incorporated through simple constitutive relationships that account for degradation of soil moduli with increasing strain values.A simple power law based on published literature is used where the soil is assumed to be nonlinear-elastic and perfectly plastic. A Tresca yield surface is assumed to develop the soil stiffness variation with different strain levels that defines the non-linearity of the soil strata. This numerical technique has been applied to a pile foundation in a two - layered soil strata for a pier supporting the bridge and solved using the software MATLAB R2019a. The analysis yields the bridge pile displacements at any depth along the length of the pile. The results of the analysis are in good agreement with the published field data and the three-dimensional finite element analysis results performed using the software ANSYS 2019R3. The methodology can be extended to study the response of the multi-strata soil supporting group piles underneath the bridge piers.

Keywords: pile foundations, deep foundations, multilayer soil strata, energy based method

Procedia PDF Downloads 129

Authors: Umit Cali

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The integration of intermittent energy sources like wind farms into the electricity grid has become an important challenge for the utilization and control of electric power systems, because of the fluctuating behaviour of wind power generation. Wind power predictions improve the economic and technical integration of large amounts of wind energy into the existing electricity grid. Trading, balancing, grid operation, controllability and safety issues increase the importance of predicting power output from wind power operators. Therefore, wind power forecasting systems have to be integrated into the monitoring and control systems of the transmission system operator (TSO) and wind farm operators/traders. The wind forecasts are relatively precise for the time period of only a few hours, and, therefore, relevant with regard to Spot and Intraday markets. In this work predictive data mining techniques are applied to identify a statistical and neural network model or set of models that can be used to predict wind power output of large onshore and offshore wind farms. These advanced data analytic methods helps us to amalgamate the information in very large meteorological, oceanographic and SCADA data sets into useful information and manageable systems. Accurate wind power forecasts are beneficial for wind plant operators, utility operators, and utility customers. An accurate forecast allows grid operators to schedule economically efficient generation to meet the demand of electrical customers. This study is also dedicated to an in-depth consideration of issues such as the comparison of day ahead and the short-term wind power forecasting results, determination of the accuracy of the wind power prediction and the evaluation of the energy economic and technical benefits of wind power forecasting.

Keywords: renewable energy sources, wind power, forecasting, data mining, big data, artificial intelligence, energy economics, power trading, power grids

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Authors: V. I. Kozyavkin, L. F. Shestopalova, T. B. Voloshyn

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Introduction: Rehabilitation of children with Autism is the issue of the day in psychiatry and neurology. It is attributed to constantly increasing quantity of autistic children - Autistic Spectrum Disorders (ASD) Existing rehabilitation approaches in treatment of children with Autism improve their medico- social and social- psychological adjustment. Experience of treatment for different kinds of Autistic disorders in International Clinic of Rehabilitation (ICR) reveals the necessity of complex intensive approach for healing this malady and wider implementation of a Kozyavkin method for treatment of children with ASD. Methods: 19 children aged from 3 to 14 years were examined. They were diagnosed ‘Autism’ (F84.0) with comorbid neurological pathology (from pyramidal insufficiency to para- and tetraplegia). All patients underwent rehabilitation in ICR during two weeks, where INRS approach was used. INRS included methods like biomechanical correction of the spine, massage, physical therapy, joint mobilization, wax-paraffin applications. They were supplemented by art- therapy, ergotherapy, rhythmical group exercises, computer game therapy, team Olympic games and other methods for improvement of motivation and social integration of the child. Estimation of efficacy was conducted using parent’s questioning and done twice- on the onset of INRS rehabilitation course and two weeks afterward. For efficacy assessment of rehabilitation of autistic children in ICR standardized tool was used, namely Autism Treatment Evaluation Checklist (ATEC). This scale was selected because any rehabilitation approaches for the child with Autism can be assessed using it. Results: Before the onset of INRS treatment mean score according to ATEC scale was 64,75±9,23, it reveals occurrence in examined children severe communication, speech, socialization and behavioral impairments. After the end of the rehabilitation course, the mean score was 56,5±6,7, what indicates positive dynamics in comparison to the onset of rehabilitation. Generally, improvement of psychoemotional state occurred in 90% of cases. Most significant changes occurred in the scope of speech (16,5 before and 14,5 after the treatment), socialization (15.1 before and 12,5 after) and behavior (20,1 before and 17.4 after). Conclusion: As a result of INRS rehabilitation course reduction of autistic symptoms was noted. Particularly improvements in speech were observed (children began to spell out new syllables, words), there was some decrease in signs of destructiveness, quality of contact with the surrounding people improved, new skills of self-service appeared. The prospect of the study is further, according to evidence- based medicine standards, deeper examination of INRS and assessment of its usefulness in treatment for Autism and ASD.

Keywords: intensive neurophysiological rehabilitation system (INRS), international clinic od rehabilitation, ASD, rehabilitation

Procedia PDF Downloads 165

Authors: Rimon Rafiah

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This paper will present a combination of two seemingly unrelated models, which are the one for estimating economic development impacts as a result of transportation investment and the other for increasing CAV penetration in order to reduce congestion. Measuring economic development impacts resulting from transportation investments is becoming more recognized around the world. Examples include the UK’s Wider Economic Benefits (WEB) model, Economic Impact Assessments in the USA, various input-output models, and additional models around the world. The economic impact model is based on WEB and is based on the following premise: investments in transportation will reduce the cost of personal travel, enabling firms to be more competitive, creating additional throughput (the same road allows more people to travel), and reducing the cost of travel of workers to a new workplace. This reduction in travel costs was estimated in out-of-pocket terms in a given localized area and was then translated into additional employment based on regional labor supply elasticity. This additional employment was conservatively assumed to be at minimum wage levels, translated into GDP terms, and from there into direct taxation (i.e., an increase in tax taken by the government). The CAV model is based on economic principles such as CAV usage, supply, and demand. Usage of CAVs can increase capacity using a variety of means – increased automation (known as Level I thru Level IV) and also by increased penetration and usage, which has been predicted to go up to 50% by 2030 according to several forecasts, with possible full conversion by 2045-2050. Several countries have passed policies and/or legislation on sales of gasoline-powered vehicles (none) starting in 2030 and later. Supply was measured via increased capacity on given infrastructure as a function of both CAV penetration and implemented technologies. The CAV model, as implemented in the USA, has shown significant savings in travel time and also in vehicle operating costs, which can be translated into economic development impacts in terms of job creation, GDP growth and salaries as well. The models have policy implications as well and can be adapted for use in Japan as well.

Keywords: CAV, economic development, WEB, transport economics

Procedia PDF Downloads 67

Authors: John Dorrell, Matthew Ambrosia, Abilash

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This paper employs econometric analysis to quantify the potential profit wind farms can receive by allocating excess wind energy to power bitcoin mining machines. Cryptocurrency mining consumes a substantial amount of electricity worldwide, and wind energy produces a significant amount of energy that is lost because of the intermittent nature of the resource. Supply does not always match consumer demand. By combining the weaknesses of these two technologies, we can improve efficiency and a sustainable path to mine cryptocurrencies. This paper uses historical wind energy from the ERCOT network in Texas and cryptocurrency data from 2000-2021, to create 4-year return on investment projections. Our research model incorporates the price of bitcoin, the price of the miner, the hash rate of the miner relative to the network hash rate, the block reward, the bitcoin transaction fees awarded to the miners, the mining pool fees, the cost of the electricity and the percentage of time the miner will be running to demonstrate that wind farms generate enough excess energy to mine bitcoin profitably. Excess wind energy can be used as a financial battery, which can utilize wasted electricity by changing it into economic energy. The findings of our research determine that wind energy producers can earn profit while not taking away much if any, electricity from the grid. According to our results, Bitcoin mining could give as much as 1347% and 805% return on investment with the starting dates of November 1, 2021, and November 1, 2022, respectively, using wind farm curtailment. This paper is helpful to policymakers and investors in determining efficient and sustainable ways to power our economic future. This paper proposes a practical solution for the problem of crypto mining energy consumption and creates a more sustainable energy future for Bitcoin.

Keywords: bitcoin, mining, economics, energy

Procedia PDF Downloads 25

Authors: Adham M. Salah, Tariq A. Nagem, Raed A. Abd-Alhameed, James M. Noras

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Recently, the demand for wireless communications systems to cover more than one frequency band (multi-band) with high data rate has been increased for both fixed and mobile services. Multiple Input Multiple Output (MIMO) technology is one of the significant solutions for attaining these requirements and to achieve the maximum channel capacity of the wireless communications systems. The main issue associated with MIMO antennas especially in portable devices is the compact space between the radiating elements which leads to limit the physical separation between them. This issue exacerbates the performance of the MIMO antennas by increasing the mutual coupling between the radiating elements. In other words, the mutual coupling will be stronger if the radiating elements of the MIMO antenna are closer. This paper presents a low–profile dual-band (2×1) MIMO antenna that works at 2.4GHz, 5.3GHz and 5.8GHz for wireless local area networks (WLAN) applications. A neutralization line (NL) technique for enhancing the isolation has been used by introducing a strip line with a length of λg/4 at the isolation frequency (2.4GHz) between the radiating elements. The overall dimensions of the antenna are 33.5 x 36 x 1.6 mm³. The fabricated prototype shows a good agreement between the simulated and measured results. The antenna impedance bandwidths are 2.38–2.75 GHz and 4.4–6 GHz for the lower and upper band respectively; the reflection coefficient and mutual coupling are better than -25 dB in both lower and higher bands. The MIMO antenna performance characteristics are reported in terms of the scattering parameters, envelope correlation coefficient (ECC), total active reflection coefficient, capacity loss, antenna gain, and radiation patterns. Analysis of these characteristics indicates that the design is appropriate for the WLAN terminal applications.

Keywords: ECC, neutralization line, MIMO antenna, multi-band, mutual coupling, WLAN

Procedia PDF Downloads 128

Authors: D. Signore, M. Ferraiuolo, P. Caramuta, O. Petrella, C. Toscano

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The Digital Image Correlation (DIC) is a newly developed optical technique that is spreading in all engineering sectors because it allows the non-destructive estimation of the entire surface deformation without any contact with the component under analysis. These characteristics make the DIC very appealing in all the cases the global deformation state is to be known without using strain gages, which are the most used measuring device. The DIC is applicable to any material subjected to distortion caused by either thermal or mechanical load, allowing to obtain high-definition mapping of displacements and deformations. That is why in the civil and the transportation industry, DIC is very useful for studying the behavior of metallic materials as well as of composite materials. DIC is also used in the medical field for the characterization of the local strain field of the vascular tissues surface subjected to uniaxial tensile loading. DIC can be carried out in the two dimension mode (2D DIC) if a single camera is used or in a three dimension mode (3D DIC) if two cameras are involved. Each point of the test surface framed by the cameras can be associated with a specific pixel of the image, and the coordinates of each point are calculated knowing the relative distance between the two cameras together with their orientation. In both arrangements, when a component is subjected to a load, several images related to different deformation states can be are acquired through the cameras. A specific software analyzes the images via the mutual correlation between the reference image (obtained without any applied load) and those acquired during the deformation giving the relative displacements. In this paper, a metrological characterization of the digital image correlation is performed on aluminum and composite targets both in static and dynamic loading conditions by comparison between DIC and strain gauges measures. In the static test, interesting results have been obtained thanks to an excellent agreement between the two measuring techniques. In addition, the deformation detected by the DIC is compliant with the result of a FEM simulation. In the dynamic test, the DIC was able to follow with a good accuracy the periodic deformation of the specimen giving results coherent with the ones given by FEM simulation. In both situations, it was seen that the DIC measurement accuracy depends on several parameters such as the optical focusing, the parameters chosen to perform the mutual correlation between the images and, finally, the reference points on image to be analyzed. In the future, the influence of these parameters will be studied, and a method to increase the accuracy of the measurements will be developed in accordance with the requirements of the industries especially of the aerospace one.

Keywords: accuracy, deformation, image correlation, mechanical analysis

Procedia PDF Downloads 304

Authors: Shoumodip Roy, Ankit Singhania, K. R. K. Rao, Ravi Shankar, M. K. Agarwal, R. V. Ramna, Uttam Singh

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The productivity of a blast furnace and the quality of the hot metal produced are significantly dependent on the smoothness and stability of furnace operation. The permeability of the furnace bed, as well as the gas flow pattern, influences the steady control of process parameters. The softening – melting zone that is formed inside the furnace contributes largely in distribution of the gas flow and the bed permeability. A better shape of softening-melting zone enhances the performance of blast furnace, thereby reducing the fuel rates and improving furnace life. Therefore, predictive model of the softening- melting zone profile can be utilized to control and improve the furnace operation. The shape of softening-melting zone depends upon the physical and chemical properties of the agglomerates and iron ore charged in the furnace. The variations in the agglomerate proportion in the burden at G Blast furnace disturbed the furnace stability. During such circumstances, it was analyzed that a w-shape softening-melting zone profile was formed inside the furnace. The formation of w-shape zone resulted in poor bed permeability and non-uniform gas flow. There was a significant increase in the heat loss at the lower zone of the furnace. The fuel demand increased, and the huge production loss was incurred. Therefore, visibility of softening-melting zone profile was necessary in order to pro-actively optimize the process parameters and thereby to operate the furnace smoothly. Using stave temperatures, a model was developed that predicted the shape of the softening-melting zone inside the furnace. It was observed that furnace operated smoothly during inverse V-shape of the zone and vice-versa during w-shape. This model helped to control the heat loss, optimize the burden distribution and lower the fuel rate at G Blast Furnace, TSL Jamshedpur. As a result of furnace stabilization productivity increased by 10% and fuel rate reduced by 80 kg/thm. Details of the process have been discussed in this paper.

Keywords: agglomerate, blast furnace, permeability, softening-melting

Procedia PDF Downloads 245

Authors: Atheer Al-Nuaimi, Harry Evdorides

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Every day many people die or get disabled or injured on roads around the world, which necessitates more specific treatments for transportation safety issues. International road assessment program (iRAP) model is one of the comprehensive road safety models which accounting for many factors that affect road safety in a cost-effective way in low and middle income countries. In iRAP model road safety has been divided into five star ratings from 1 star (the lowest level) to 5 star (the highest level). These star ratings are based on star rating score which is calculated by iRAP methodology depending on road attributes, traffic volumes and operating speeds. The outcome of iRAP methodology are the treatments that can be used to improve road safety and reduce fatalities and serious injuries (FSI) numbers. These countermeasures can be used separately as a single countermeasure or mix as multiple countermeasures for a location. There is general agreement that the adequacy of a countermeasure is liable to consistent losses when it is utilized as a part of mix with different countermeasures. That is, accident diminishment appraisals of individual countermeasures cannot be easily added together. The iRAP model philosophy makes utilization of a multiple countermeasure adjustment factors to predict diminishments in the effectiveness of road safety countermeasures when more than one countermeasure is chosen. A multiple countermeasure correction factors are figured for every 100-meter segment and for every accident type. However, restrictions of this methodology incorporate a presumable over-estimation in the predicted crash reduction. This study aims to adjust this correction factor by developing new models to calculate the effect of using multiple countermeasures on the number of fatalities for a location or an entire road. Regression models have been used to establish relationships between crash frequencies and the factors that affect their rates. Multiple linear regression, negative binomial regression, and Poisson regression techniques were used to develop models that can address the effectiveness of using multiple countermeasures. Analyses are conducted using The R Project for Statistical Computing showed that a model developed by negative binomial regression technique could give more reliable results of the predicted number of fatalities after the implementation of road safety multiple countermeasures than the results from iRAP model. The results also showed that the negative binomial regression approach gives more precise results in comparison with multiple linear and Poisson regression techniques because of the overdispersion and standard error issues.

Keywords: international road assessment program, negative binomial, road multiple countermeasures, road safety

Procedia PDF Downloads 233