Search results for: green infrastructure network

Authors: Ying Bo Xie, Hisa Yuki Kurokawa

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Due to the fast development, China's express delivery industry has involved in a dilemma that the service quality are keeping decreasing while the construction rate of delivery network cannot meet the customers’ demand. In order to get out of this dilemma and enjoy a succession development rate, it is necessary to understand the current situation of China's express delivery industry. Firstly, the evolution of China's express delivery industry was systematical presented. Secondly, according to the number of companies and the amount of parcels they has dealt each year, the merits and faults of tow kind of operating pattern was analyzed. Finally, based on the characteristics of these express companies, the problems of China's express delivery industry was divided into several types and the countermeasures were given out respectively.

Keywords: China, express delivery industry, status, problem

Procedia PDF Downloads 367

Authors: M. Chumburidze, N. Salia, T. Namchevadze

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This work deal with case processing problems in business. The task of strategic credit requirements management of cases portfolio is discussed. The information model of credit requirements in a binary tree diagram is considered. The algorithms to solve issues of prioritizing clusters of cases in business have been investigated. An implementation of priority queues to support case management operations has been presented. The corresponding pseudo codes for the programming application have been constructed. The tools applied in this development are based on binary tree ordering algorithms, optimization theory, and business management methods.

Keywords: credit network, case portfolio, binary tree, priority queue, stack

Procedia PDF Downloads 155

Authors: Mojtaba Javdan, Kamran Jafarpour Ghalehteimouri, Moslem Ghasemi, Arezu Riazi

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Tourism has a huge multiplier effect on other socioeconomic sectors, resulting in better infrastructure and public services. However, its environmental impact is still a source of concern. As a result, a greater emphasis has been placed on improving the sustainability of tourist destinations. Despite the global significance of sustainability assessment, only a few widely accepted methods for measuring sustainability exist. As a result, the life cycle concept is used to evaluate environmental, economic, and social consequences. The Social Life Cycle Assessment (S-LCA) is a crucial life cycle tool. Due to the tourism-specific service specifications, tourism-related activities are well-suited for the elaboration of data related to social sustainability. Therefore, the possibility of how the S-LCA is involved in ensuring cultural tourism destinations' long-term viability can be the main question. To answer this question, this article examines the theoretical evolution of both the S-LCA and cultural tourism. Potential application gaps are investigated, and an S-LCA framework for sustainable cultural tourism destinations is proposed and discussed. Thus, by bringing all stakeholders' interests together, the proposed S-LCA conceptual framework can play an effective role in achieving the principles and objectives of sustainable tourism destination management.

Keywords: social life cycle assessment, sustainable cultural tourism destinations, sustainable tourism destination management, S-LCA framework

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Authors: Namrata Ghosh, Sapan Tiwari

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Public transport service plays an important role in people's transportation needs in urban areas. Bus Rapid Transit System (BRTS) is a transport service that provides passengers with a quick and efficient mode of transport. It is developed by changing the existing infrastructure, vehicles, route, or by developing a new dedicated corridor for the bus route. This dedicated lanes transport passengers to their destination quickly and efficiently and flexible in meeting demand. However, with rapid urbanization and increasing population density in Indian cities, traffic congestion has become a significant issue. In a few Indian cities, the BRTS concept is implemented to address the issue of traffic congestion that eventually resulted in less road congestion. The research aims to provide a literature review on the overall outlook of the BRTS system and its practical implementation in mass urban transit. First, it reflects a literature review on the concept of the BRTS system in both developed and developing countries. Afterward, comparative analysis of BRTS, hindrances associated with the permanent integrated system, and the need for establishing the Bus Rapid Transit System in Indian cities is demonstrated. The research concludes with some recommendations that could help in improving the loopholes in the existing system.

Keywords: bus rapid transit system(BRTS), dedicated corridor, public transport, traffic congestion

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Authors: N. Zerzouri, N. Benalia, N. Bensiali

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This work is devoted to an analysis of the operation of a doubly fed induction generator (DFIG) integrated with a wind system. The power transfer between the stator and the network is carried out by acting on the rotor via a bidirectional signal converter. The analysis is devoted to the study of a fault in the converter due to an interruption of the control of a semiconductor. Simulation results obtained by the MATLAB / Simulink software illustrate the quality of the power generated at the default.

Keywords: doubly fed induction generator (DFIG), wind power generation, back to back PWM converter, default switching

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Authors: Chamindi Dilkushi Senaratne

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Staff development both formal and informal, structured and unstructured is universally accepted as fundamental to the growth of individuals and institutions. This study is based on feedback summaries collected from 2014 to 2017 from 240 participants of the staff development programme for probationary lecturers at the University of Kelaniya, Sri Lanka. It also contains data from interviews conducted with the resource persons in the programme. The study further includes observations from experts involved in staff training in higher education institutions in Sri Lanka The data reveals that though the programme has many aspects that can be improved, the selected topics in the curriculum and new topics that were incorporated had positive impacts to enhance continuing professional development of staff in Sri Lankan universities. The participants also believe that the programme has an impact on professional development, teaching, and management of classroom and curricula and research skills. Based on the findings, the study recommends the addition of new topics to the curriculum such as continuing professional development, code of conduct in universities, gender awareness and the green concept. The study further recommends programmes for senior academic staff in universities to assist them to reach higher levels in their career by focusing on areas such as teaching, research, and administrative skills.

Keywords: staff development, higher education, curriculum, research

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Authors: Sakshi Dhumale, Madhushree C., Amba Shetty

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The effect of station density on the geostatistical prediction of groundwater levels is of critical importance to ensure accurate and reliable predictions. Monitoring station density directly impacts the accuracy and reliability of geostatistical predictions by influencing the model's ability to capture localized variations and small-scale features in groundwater levels. This is particularly crucial in regions with complex hydrogeological conditions and significant spatial heterogeneity. Insufficient station density can result in larger prediction uncertainties, as the model may struggle to adequately represent the spatial variability and correlation patterns of the data. On the other hand, an optimal distribution of monitoring stations enables effective coverage of the study area and captures the spatial variability of groundwater levels more comprehensively. In this study, we investigate the effect of station density on the predictive performance of groundwater levels using the geostatistical technique of Ordinary Kriging. The research utilizes groundwater level data collected from 121 observation wells within the semi-arid Berambadi watershed, gathered over a six-year period (2010-2015) from the Indian Institute of Science (IISc), Bengaluru. The dataset is partitioned into seven subsets representing varying sampling densities, ranging from 15% (12 wells) to 100% (121 wells) of the total well network. The results obtained from different monitoring networks are compared against the existing groundwater monitoring network established by the Central Ground Water Board (CGWB). The findings of this study demonstrate that higher station densities significantly enhance the accuracy of geostatistical predictions for groundwater levels. The increased number of monitoring stations enables improved interpolation accuracy and captures finer-scale variations in groundwater levels. These results shed light on the relationship between station density and the geostatistical prediction of groundwater levels, emphasizing the importance of appropriate station densities to ensure accurate and reliable predictions. The insights gained from this study have practical implications for designing and optimizing monitoring networks, facilitating effective groundwater level assessments, and enabling sustainable management of groundwater resources.

Keywords: station density, geostatistical prediction, groundwater levels, monitoring networks, interpolation accuracy, spatial variability

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Authors: Ahmet Faruk Akyuz, Hasan Sakir Bilge

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Depth estimation using stereo images is a challenging problem in computer vision. Many different studies have been carried out to solve this problem. With advancing machine learning, tackling this problem is often done with neural network-based solutions. The images used in these studies are mostly in the visible spectrum. However, the need to use the Infrared (IR) spectrum for depth estimation has emerged because it gives better results than visible spectra in some conditions. At this point, we recommend using thermal-thermal (IR) image pairs for depth estimation. In this study, we used two well-known networks (PSMNet, FADNet) with minor modifications to demonstrate the viability of this idea.

Keywords: thermal stereo matching, deep neural networks, CNN, Depth estimation

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Authors: Azeez O. Idris, Benjamin O. Orimolade, Potlako J. Mafa, Alex T. Kuvarega, Usisipho Feleni, Bhekie B. Mamba

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We report a simple and cheaper method for the electrochemical detection of Se(IV) using carbon nanodots (CNDTs) prepared from oat. The carbon nanodots were synthesised by green and facile approach and characterised using scanning electron microscopy, high-resolution transmission electron microscopy, Fourier transform infrared spectroscopy, X-ray diffraction, and Raman spectroscopy. The CNDT was used to fabricate an electrochemical sensor for the quantification of Se(IV) in water. The modification of glassy carbon electrode (GCE) with carbon nanodots led to an increase in the electroactive surface area of the electrode, which enhances the redox current peak of [Fe(CN)₆]₃₋/₄‒ in comparison to the bare GCE. Using the square wave voltammetry, the detection limit and quantification limit of 0.05 and 0.167 ppb were obtained under the optimised parameters using deposition potential of -200 mV, 0.1 M HNO₃ electrolyte, electrodeposition time of 60 s, and pH 1. The results further revealed that the GCE-CNDT was not susceptible to many interfering cations except Cu(II) and Pb(II), and Fe(II). The sensor fabrication involves a one-step electrode modification and was used to detect Se(IV) in a real water sample, and the result obtained is in agreement with the inductively coupled plasma technique. Overall, the electrode offers a cheap, fast, and sensitive way of detecting selenium in environmental matrices.

Keywords: carbon nanodots, square wave voltammetry, nanomaterials, selenium, sensor

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Authors: Nipun Manirajan, Heeralal Gargama, Sushil Guhe, Manoj Prabhakaran

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In railway industry, train sets are designed based on contractual requirements (mission profile), where reliability targets are measured in terms of mean distance between failures (MDBF). However, during the beginning of revenue services, trains do not achieve the designed mission profile distance (mileage) within the timeframe due to infrastructure constraints, scarcity of commuters or other operational challenges thereby not respecting the original design inputs. Since trains do not run sufficiently and do not achieve the designed mileage within the specified time, car builder has a risk of not achieving the contractual MDBF target. This paper proposes a constant failure rate based model to deal with the situations where mileage accumulation is not a part of the design mission profile. The model provides appropriate MDBF target to be demonstrated based on actual accumulated mileage. A case study of rolling stock running in the field is undertaken to analyze the failure data and MDBF target demonstration during low mileage accumulation. The results of case study prove that with the proposed method, reliability targets are achieved under low mileage accumulation.

Keywords: mean distance between failures, mileage-based reliability, reliability target appropriations, rolling stock reliability

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Authors: Richard Joseph, Nerey Mvungi

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An electric power system includes a generating, a transmission, a distribution and consumers subsystems. An electrical power network in Tanzania keeps growing larger by the day and become more complex so that, most utilities have long wished for real-time monitoring and remote control of electrical power system elements such as substations, intelligent devices, power lines, capacitor banks, feeder switches, fault analyzers and other physical facilities. In this paper, the concept of automation of management of power systems from generation level to end user levels was determined by using Power System Simulator for Engineering (PSS/E) version 30.3.2.

Keywords: automation, distribution subsystem, generating subsystem, PSS/E, TANESCO, transmission subsystem

Procedia PDF Downloads 679

Authors: Ebrahim Alrashed, Yousef Rafique

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Mobile devices have drastically changed the way we do things on the move. They are being extremely relied on to perform tasks that are analogous to desktop computer capability. There has been a rapid increase of computational power on these devices; however, battery technology is still the bottleneck of evolution. The primary modern approach day approach to tackle this issue is offloading computation to the cloud, proving to be latency expensive and requiring high network bandwidth. In this paper, we explore efforts to perform barter-based mobile-to-mobile offloading. We present define a protocol and present an architecture to facilitate the development of such a system. We further highlight the deployment and security challenges.

Keywords: computational offloading, power conservation, cloud, sandboxing

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Authors: Hadjer Didouh, Mohammed Hadj Melliani, Izzeddine Sameut Bouhaik

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Microbiologically influenced corrosion (MIC) is a major cause of pipeline failure in the oil and gas industry, particularly affecting carbon steel, which is widely used for its cost-effectiveness and mechanical properties. This study investigates the adhesion of sulfate-reducing bacteria (SRB) and other corrosive microbial species on API 5L X52 carbon steel in crude oil and injection water environments. Experimental results showed that after 72 hours of exposure, biofilm formed extensively, leading to significant corrosion rates. Weight loss measurements indicated a corrosion rate of 0.39 mm/year, with localized pitting observed at depths reaching 120 μm. Electrochemical impedance spectroscopy (EIS) revealed a drastic decrease in charge transfer resistance, from 1200 Ω/cm² for sterile samples to 240 Ω/cm² in the presence of SRB biofilm. Scanning electron microscopy (SEM) and energy dispersive spectroscopy (EDS) analyses confirmed the presence of iron sulfide deposits, indicating active bacterial colonization and biofilm-induced pitting corrosion. This study highlights the severe impact of MIC on pipeline infrastructure, emphasizing the need for efficient microbial control strategies. Furthermore, the results provide a framework for the development of enhanced protective coatings and environmentally friendly biocides to mitigate the economic and environmental risks associated with MIC in oilfield operations in Algeria.

Keywords: MIC, corrosion, bacteria, API 5L X52

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Authors: Atef A. Ata, Sohair F. Rezeka, Ahmed El-Shenawy, Mohammed Diab

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Image processing in today’s world grabs massive attentions as it leads to possibilities of broaden application in many fields of high technology. The real challenge is how to improve existing sorting system applications which consists of two integrated stations of processing and handling with a new image processing feature. Existing color sorting techniques use a set of inductive, capacitive, and optical sensors to differentiate object color. This research presents a mechatronics color sorting system solution with the application of image processing. A 5-DOF robot arm is designed and developed with pick and place operation to be main part of the color sorting system. Image processing procedure senses the circular objects in an image captured in real time by a webcam attached at the end-effector then extracts color and position information out of it. This information is passed as a sequence of sorting commands to the manipulator that has pick-and-place mechanism. Performance analysis proves that this color based object sorting system works very accurate under ideal condition in term of adequate illumination, circular objects shape and color. The circular objects tested for sorting are red, green and blue. For non-ideal condition, such as unspecified color the accuracy reduces to 80%.

Keywords: robotics manipulator, 5-DOF manipulator, image processing, color sorting, pick-and-place

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Authors: Yangzhi Li

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Network transfer of information and performance customization is now a viable method of digital industrial production in the era of Industry 4.0. Robot platforms and network platforms have grown more important in digital design and construction. The pressing need for novel building techniques is driven by the growing labor scarcity problem and increased awareness of construction safety. Robotic approaches in construction research are regarded as an extension of operational and production tools. Several technological theories related to robot autonomous recognition, which include high-performance computing, physical system modeling, extensive sensor coordination, and dataset deep learning, have not been explored using intelligent construction. Relevant transdisciplinary theory and practice research still has specific gaps. Optimizing high-performance computing and autonomous recognition visual guidance technologies improves the robot's grasp of the scene and capacity for autonomous operation. Intelligent vision guidance technology for industrial robots has a serious issue with camera calibration, and the use of intelligent visual guiding and identification technologies for industrial robots in industrial production has strict accuracy requirements. It can be considered that visual recognition systems have challenges with precision issues. In such a situation, it will directly impact the effectiveness and standard of industrial production, necessitating a strengthening of the visual guiding study on positioning precision in recognition technology. To best facilitate the handling of complicated components, an approach for the visual recognition of parts utilizing machine learning algorithms is proposed. This study will identify the position of target components by detecting the information at the boundary and corner of a dense point cloud and determining the aspect ratio in accordance with the guidelines for the modularization of building components. To collect and use components, operational processing systems assign them to the same coordinate system based on their locations and postures. The RGB image's inclination detection and the depth image's verification will be used to determine the component's present posture. Finally, a virtual environment model for the robot's obstacle-avoidance route will be constructed using the point cloud information.

Keywords: robotic construction, robotic assembly, visual guidance, machine learning

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Authors: Sofia Figueroa, Efrain Bernal, Silvia Del Pilar Forero, Humberto Ramirez

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This work shows a detailed review of the scope of global research on the road infrastructure using materials from Construction and Demolition Waste (C&DW), also called RCD. In the first phase of this research, a segment of road was designed using recycled materials such as Reclaimed Asphalt Pavement (RAP) on the top, the natural coarse base including 30% of RAP and recycled concrete blocks. The second part of this segment was designed using regular materials for each layer of the pavement. Both structures were built next to each other in order to analyze and measure the material properties as well as performance and environmental factors in the pavement under real traffic and weather conditions. Different monitoring devices were installed among the structure, based on the literature revision, such as soil cells, linear potentiometer, moisture sensors, and strain gauges that help us to know the C&DW as a part of the pavement structure. This research includes not only the physical characterization but also the measured parameters in a field such as an asphalt mixture (RAP) strain (ετ), vertical strain (εᵥ) and moisture control in coarse layers (%w), and the applied loads and strain in the subgrade (εᵥ). The results will show us what is happening with these materials in order to obtain not only a sustainable solution but also to know its behavior and lifecycle.

Keywords: sustainable pavements, construction & demolition waste-C&DW, recycled rigid concrete, reclaimed asphalt pavement-rap

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Authors: Benjamin Cramer, Josephine Chau, Helen Little, Erica Randle

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Play Streets provide a safe and open space for children to play and adults to socialize by closing residential streets to through traffic. While research on Play Streets has typically focused on physical activity outcomes in children, there is limited research on the psychosocial health externalities for the wider community. Charles Sturt, a local government area in Adelaide, South Australia, has been hosting Play Streets for several years. The current study is a mixed-methods evaluation of the Charles Sturt Play Streets, concerned with the perceived psychological and social impacts that Play Streets impact on the community. A combination of semi-structured interviews of Play Street organizers and participants will be conducted and analyzed using inductive thematic analysis. Pre-existing survey data will also be analyzed quantitatively and qualitatively to triangulate the findings of the qualitative interviews. The implications of this research are far-reaching, from informing local councils of any additional health benefits of Play Streets, expanding the growing literature on Play Streets beyond childhood physical activity, informing the development of city infrastructure, and advancing the Sustainability Development Goals of Good Health and Wellbeing, Reduced Inequalities, and Sustainable Cities and Communities.

Keywords: play streets, mental health, social health, community health

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Authors: Pravallika Chithuloori, Jin-Man Kim

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Soil liquefaction, triggered by increased porewater pressure, poses a significant threat to infrastructure stability in seismically active regions, and its forecasting remains challenging due to intricate nonlinear interactions. This study uses a dataset of 540 samples that includes seismic parameters and standard penetration test (SPT) results to evaluate liquefaction prediction. SPT N60 values, soil fine content (FC), ground water table (GWT), effective stress of overburden (ESO), peak ground acceleration (PGA), and earthquake magnitude (Mw) are key inputs. A gradient boost classifier (GBC) machine learning (ML) model was utilized to classify liquefaction events. The model’s performance was evaluated using metrics such as accuracy, precision, recall, F1-score, confusion matrix analysis, sensitivity analysis, feature importance ranking, and Shapley Additive Explanations (SHAP). According to these evaluations, the most significant variables in predicting liquefaction were PGA, SPT-N60, and GWT. The robustness of the GBC model was further validated through precision-recall curves and k-fold cross-validation, and it achieved an impressive 99.38% prediction accuracy. These results highlight the potential of the GBC technique to advance the reliability of liquefaction forecasting.

Keywords: liquefaction, standard penetration test, gradient boost, machine learning, SHAP

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Authors: John Hang Leung, Shyh-Yau Wang, Hei-Tung Yip, Fion, Ho Tsung-chin, Agnes LF Chan

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Objectives: Platinum-resistant ovarian cancer has a short overall survival of 9–12 months and limited treatment options. The combination of immunotherapy and targeted therapy appears to be a promising treatment option for patients with ovarian cancer, particularly to patients with platinum-resistant recurrent ovarian cancer (PRrOC). However, there are no direct head-to-head clinical trials comparing their efficacy and toxicity. We, therefore, used a network to directly and indirectly compare seven newer immunotherapies or targeted therapies combined with chemotherapy in platinum-resistant relapsed ovarian cancer, including antibody-drug conjugates, PD-1 (Programmed death-1) and PD-L1 (Programmed death-ligand 1), PARP (Poly ADP-ribose polymerase) inhibitors, TKIs (Tyrosine kinase inhibitors), and antiangiogenic agents. Methods: We searched PubMed (Public/Publisher MEDLINE), EMBASE (Excerpta Medica Database), and the Cochrane Library electronic databases for phase II and III trials involving PRrOC patients treated with immunotherapy or targeted therapy plus chemotherapy. The quality of included trials was assessed using the GRADE method. The primary outcomes compared were progression-free survival, the secondary outcomes were overall survival and safety. Results: Seven randomized controlled trials involving a total of 2058 PRrOC patients were included in this analysis. Bevacizumab plus chemotherapy showed statistically significant differences in PFS (Progression-free survival) but not OS (Overall survival) for all interested targets and immunotherapy regimens; however, according to the heatmap analysis, bevacizumab plus chemotherapy had a statistically significant risk of ≥grade 3 SAEs (Severe adverse effects), particularly hematological severe adverse events (neutropenia, anemia, leukopenia, and thrombocytopenia). Conclusions: Bevacizumab plus chemotherapy resulted in better PFS as compared with all interested regimens for the treatment of PRrOC. However, statistical differences in SAEs as bevacizumab plus chemotherapy is associated with a greater risk for hematological SAE.

Keywords: platinum-resistant recurrent ovarian cancer, network meta-analysis, immune checkpoint inhibitors, target therapy, antiangiogenic agents

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Authors: Shervin Alaei, Reza Moradi

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Trading in financial markets is subject to risks due to their high volatilities. Here, using an LSTM neural network, and by doing some risk-based feature engineering tasks, we developed a method that can accurately predict trends of the Tehran stock exchange market index from a few days ago. Our test results have shown that the proposed method with an average prediction accuracy of more than 94% is superior to the other common machine learning algorithms. To the best of our knowledge, this is the first work incorporating deep learning and risk factors to accurately predict market trends.

Keywords: deep learning, LSTM, trend prediction, risk management, artificial neural networks

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Authors: Osama M. Darwesh, Sahar H. Hassan, Abd El-Raheem R. El-Shanshoury, Shawky Z. Sabae

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Nanotechnology as multidisciplinary technology is growing rapidly with important applications in several sectors. Also, nanobiotechnology is known for the use of microorganisms for the synthesis of targeted nanoparticles. The present study deals with the green synthesis of silver nanoparticles using aquatic bacteria and the development of a biogenic nanocomposite for environmental applications. Twenty morphologically different colonies were isolated from the collected water samples from eight different locations at the Rosetta branch of the Nile Delta, Egypt. The obtained results illustrated that the most effective bacterial isolate (produced the higher amount of AgNPs after 24 h of incubation time) is isolate R3. Bacillus tequilensis was the strongest extracellular bio-manufactory of AgNPs. Biosynthesized nanoparticles had a spherical shape with a mean diameter of 2.74 to 28.4 nm. The antimicrobial activity of silver nanoparticles against many pathogenic microbes indicated that the produced AgNPs had high activity against all tested multi-antibiotic resistant pathogens. Also, the stabilized prepared AgNPs-SA nanocomposite has greater catalytic activity for the decolourization of some dyes like Methylene blue (MB) and Crystal violet. Such results represent a promising stage for producing eco-friendly, cost-effective, and easy-to-handle devices for the bioremediation of contaminated industrial wastewater.

Keywords: bioremediation, AgNPs, AgNPs-SA nanocomposite, Bacillus tequilensis, nanobiotechnology

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Authors: Mohsen Abotalebi Esfahani, Amin Rahimi

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Maintenance and management of route and roads infrastructure is one of the most important and the most fundamental principles of the countries. Several methods have been under investigation as preventive proceedings for the maintenance of asphalt pavements for many years. Using a mixture of salt, sand and gravel is the most common method of deicing, which could have numerous harmful consequences. Icy or snow-covered road is one of the major reasons of accidents in rainy seasons, which causes substantial damages such as loss of time and energy, environmental pollution, destruction of buildings, traffic congestion and rising possibility of accidents. Regarding this, every year the government incurred enormous costs to secure traverses. In this study, asphalt pavements have been cured, in terms of compressive strength, tensile strength and resilient modulus of asphalt samples, under the influence of Magnesium Chloride, Calcium Chloride, Sodium Chloride, Urea and pure water; and showed that de-icing with the calcium chloride solution and urea have the minimum negative effect and de-icing with pure water has most negative effect on laboratory specimens. Hence some simple techniques and new equipment and less use of sand and salt, can reduce significantly the risks and harmful effects of excessive use of salt, sand and gravel and at the same time use the safer roads.

Keywords: maintenance, sodium chloride, icyroad, calcium chloride

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Authors: Orpita U. Laz, Ataur Rahman

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Climate change will affect various aspects of hydrological cycle such as rainfall. A change in rainfall will affect flood magnitude and frequency in future which will affect the design and operation of hydraulic structures. In this paper, trends in sub-hourly, sub-daily, and daily extreme rainfall events from 18 rainfall stations located in Tasmania, Australia are examined. Two non-parametric tests (Mann-Kendall and Spearman’s Rho) are applied to detect trends at 10%, 5%, and 1% significance levels. Sub-hourly (6, 12, 18, and 30 minutes) annual maximum rainfall events have been found to experience statistically significant upward trends at 10 % level of significance. However, sub-daily durations (1 hour, 3 and 12 hours) exhibit decreasing trends and no trends exists for longer duration rainfall events (e.g. 24 and 72 hours). Some of the durations (e.g. 6 minutes and 6 hours) show similar results (with upward trends) for both the tests. For 12, 18, 60 minutes and 3 hours durations both the tests show similar downward trends. This finding has important implication for Tasmania in the design of urban infrastructure where shorter duration rainfall events are more relevant for smaller urban catchments such as parking lots, roof catchments and smaller sub-divisions.

Keywords: climate change, design rainfall, Mann-Kendall test, trends, Spearman’s Rho, Tasmania

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Authors: Fahd Hamad Alqasham

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This longitudinal study investigates Saudi students’ use trajectory and experiences of Twitter as an innovative tool for in-class learning of the English language in a Saudi tertiary English as a foreign language (EFL) context for a 12-week semester. The study adopted van Lier’s agency theory (2008, 2010) as the analytical framework to obtain an in-depth analysis of how the learners’ could utilize Twitter to create innovative ways for them to engage in English learning inside the language classroom. The study implemented a mixed methods approach, including six data collection instruments consisting of a research log, observations, focus group participation, initial and post-project interviews, and a post-project questionnaire. The study was conducted at Qassim University, specifically at Preparatory Year Program (PYP) on the main campus. The sample included 25 male students studying in the first level of PYP. The findings results revealed that although Twitter’s affordances initially paled a crucial role in motivating the learners to initiate their agency inside the classroom to learn English, the contextual constraints, mainly anxiety, the university infrastructure, and the teacher’s role negatively influenced the sustainability of Twitter’s use past week nine of its implementation.

Keywords: CALL, agency, innovation, EFL, language learning

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Authors: Stephen Eyije Abechi, Casimir Emmanuel Gimba, Zaharaddeen Nasiru Garba, Sani Shamsudeen, David Ebuka Authur

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Corrosion inhibition of mild steel in acid medium using Acalypha chamaedrifolia leaves extract as potential green inhibitor was investigated. Gravimetric (weight loss) technique was used for the corrosion studies. Mild steel coupons of 2cm × 1cm × 0.27 cm dimensions were exposed for varying durations of between 24 to 120 hours, in 1M HCl medium containing a varying concentrations of the leaves extract (0.25g/L, - 1.25g/L). The results show that corrosion rates dropped from a value of 0.49 mgcm-2hr-1 for the uninhibited medium to a value of 0.15 mgcm-2hr-1 for the inhibited medium of 1M HCl in 0.25 g/l of the extract. Values of corrosion inhibition efficiencies of 70.38-85.11% were observed as the concentration of the inhibitor were increased from 0.25g/L, - 1.25g/L. Corrosion Inhibition was found to increase with increase in immersion time and temperature. The magnitude of the Ea indicates that the interaction between the metal surface and the inhibitor was chemisorptions. The Adsorption process fit into the Langmuir isotherm model with a correlation coefficient of 0.97. Evidence from molecular dynamics model shows that Methyl stearate (Line 5) and (3Z, 13Z)-2-methyloctadeca-3,13-dien-1-ol (line 11) were found to have the highest binding energy of -197.69 ± 3.12 and-194.56 ± 10.04 in kcal/mol respectively. The binding energy of these compounds indicates that they would be a very good corrosion inhibitor for mild steel and other Fe related materials.

Keywords: binding energy, corrosion, inhibitor, langmuir isotherm, mild steel

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Authors: Gizachew Belay Adugna, Yu-Tai Tao

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Perovskite solar cells (PSCs) have been gaining impressive progress with excellent power conversion efficiency (PCE) of 25.5% in small-area devices. However, the conventional film coating approach is not applicable to large-area module fabrication. Meniscus-guided coating, including blade coating, slot-die coating, and bar coating, is solution processing and promising for large-area and cost-effective film coating to industrial-scale PSCs. Here, we develop simple and scalable solution shearing (SS) and bar coating (BC) methods to coat all layers on large-area (10x10 cm²) substrate in FTO/c-TiO₂/mp-TiO₂/ CH₃NH₃PbI₃/Spiro-OMeTAD/Ag device structure, except the Ag electrode. All solution-sheared PSC exhibited a champion power conversion efficiency of 15.89% in the conational DMF/DMSO solvent. Whereas a very high PCE of 20.30% compared to the controlled spin-coated device (SC, 17.60%) was achieved from the large area sheared perovskite film in a green ACN/MA solvent. Similarly, a remarkable PCE of 18.50% was achieved for a device fabricated from a large-area perovskite film in a simpler and more compatible Bar-coating system. This strategy demonstrates the huge potential for module fabrication and future PSC commercialization.

Keywords: Perovskite solar cells, larger area film coating, meniscus-guided film coating, solution-shearing, bar-coating, power conversion efficiency

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Authors: Fathi Abid, Bilel Kaffel

Abstract:

The olive tree, the olive harvest during winter season and the production of olive oil better known by professionals under the name of the crushing operation have interested institutional traders such as olive-oil offices and private companies such as food industry refining and extracting pomace olive oil as well as export-import public and private companies specializing in olive oil. The major problem facing producers of olive oil each winter campaign, contrary to what is expected, it is not whether the harvest will be good or not but whether the sale price will allow them to cover production costs and achieve a reasonable margin of profit or not. These questions are entirely legitimate if we judge by the importance of the issue and the heavy complexity of the uncertainty and competition made tougher by a high level of indebtedness and the experience and expertise of speculators and producers whose objectives are sometimes conflicting. The aim of this paper is to study the formation mechanism of olive oil prices in order to learn about speculators’ behavior and expectations in the market, how they contribute by their industry knowledge and their financial alliances and the size the financial challenge that may be involved for them to build private information hoses globally to take advantage. The methodology used in this paper is based on two stages, in the first stage we study econometrically the formation mechanisms of olive oil price in order to understand the market participant behavior by implementing ARMA, SARMA, GARCH and stochastic diffusion processes models, the second stage is devoted to prediction purposes, we use a combined wavelet- ANN approach. Our main findings indicate that olive oil market participants interact with each other in a way that they promote stylized facts formation. The unstable participant’s behaviors create the volatility clustering, non-linearity dependent and cyclicity phenomena. By imitating each other in some periods of the campaign, different participants contribute to the fat tails observed in the olive oil price distribution. The best prediction model for the olive oil price is based on a back propagation artificial neural network approach with input information based on wavelet decomposition and recent past history.

Keywords: olive oil price, stylized facts, ARMA model, SARMA model, GARCH model, combined wavelet-artificial neural network, continuous-time stochastic volatility mode

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Authors: Abubakar Salisu Garba

Abstract:

The objective of the paper is to highlight the nature of entrepreneurial activities in the Sub Sahara Africa. Five countries in the Sub Sahara African that are participating in Global Entrepreneurship Monitor (GEM) research have been studied to understand the types of entrepreneurial activities and their socio-economic implications in the region. The importance of entrepreneurial activities in boosting socio-economic development has been recognized not only in developing countries, but across the entire global economies. Some people believe that the wealth and poverty of developing countries is associated with nature and type of entrepreneurial activity. Policy makers are not only concern about the rate of business start up, but the growth and development of those starts up is of paramount importance to the development of the country’s economy. Although, the supply of entrepreneurs is essential, sometimes it does not really matters in boosting economic performance. What is more important is having high impact entrepreneurs who could make meaningful contribution to the economy. High growth oriented entrepreneurs are more stable and contribute greatly in enhancing the economic performance. When entrepreneurs are facing difficulties in sustaining and growing their businesses, it may be unlikely for entrepreneurship to reduce unemployment and poverty. Inadequate financial supports, insufficient infrastructure, lack of enforcing laws protecting the right of entrepreneurs are some of the problems making business environment difficult in Sub-Saharan Africa.

Keywords: entrepreneurship, entrepreneurial activity, job creation, poverty reduction, Sub-Saharan Africa

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Authors: M. Pramila Devi, N. V. N. Indra Kiran

Abstract:

Control charts, predominantly in the form of X-bar chart, are important tools in statistical process control (SPC). They are useful in determining whether a process is behaving as intended or there are some unnatural causes of variation. A process is out of control if a point falls outside the control limits or a series of point’s exhibit an unnatural pattern. In this paper, a study is carried out on four training algorithms for CCPs recognition. For those algorithms optimal structure is identified and then they are studied for type I and type II errors for generalization without early stopping and with early stopping and the best one is proposed.

Keywords: control chart pattern recognition, neural network, backpropagation, generalization, early stopping

Procedia PDF Downloads 376

Authors: Basel Halak, Christian Hall, Syed Abdul Father, Nelson Chow Wai Kit, Ruwaydah Widaad Raymode

Abstract:

The attack surface on computing devices is becoming very sophisticated, driven by the sheer increase of interconnected devices, reaching 50B in 2025, which makes it easier for adversaries to have direct access and perform well-known physical attacks. The impact of increased security vulnerability of electronic systems is exacerbated for devices that are part of the critical infrastructure or those used in military applications, where the likelihood of being targeted is very high. This continuously evolving landscape of security threats calls for a new generation of defense methods that are equally effective and adaptive. This paper proposes an intelligent defense mechanism to protect from physical tampering, it consists of a tamper detection system enhanced with machine learning capabilities, which allows it to recognize normal operating conditions, classify known physical attacks and identify new types of malicious behaviors. A prototype of the proposed system has been implemented, and its functionality has been successfully verified for two types of normal operating conditions and further four forms of physical attacks. In addition, a systematic threat modeling analysis and security validation was carried out, which indicated the proposed solution provides better protection against including information leakage, loss of data, and disruption of operation.

Keywords: anti-tamper, hardware, machine learning, physical security, embedded devices, ioT

Procedia PDF Downloads 158