Search results for: efficient crow search algorithm

Authors: Laurent Pitteloud, Jörg Meier

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Piled raft foundations represent an efficient and reliable technique for transferring high vertical and horizontal loads to the subsoil. Piled raft foundations were success­fully implemented for several high-rise buildings world­wide over the last decades. For the structural design of this foundation type the stiffnesses of both the piles and the raft have to be deter­mined for the static (e.g. dead load, live load) and the dynamic load cases (e.g. earthquake). In this context the question often arises, to which proportion wind loads are to be considered as dynamic loads. Usually a piled raft foundation has to be monitored in order to verify the design hypotheses. As an additional benefit, the analysis of this monitoring data may lead to a better under­standing of the behaviour of this foundation type for future projects in similar subsoil conditions. In case the measurement frequency is high enough, one may also draw conclusions on the effect of wind loading on the piled raft foundation. For a 41-storey office building in Basel, Switzerland, the preliminary design showed that a piled raft foundation was the best solution to satisfy both design requirements, as well as economic aspects. A high-frequency monitoring of the foundation including pile loads, vertical stresses under the raft, as well as pore water pressures was performed over 5 years. In windy situations the analysis of the measure­ments shows that the pile load increment due to wind consists of a static and a cyclic load term. As piles and raft react with different stiffnesses under static and dynamic loading, these measure­ments are useful for the correct definition of stiffnesses of future piled raft foundations. This paper outlines the design strategy and the numerical modelling of the aforementioned piled raft foundation. The measurement results are presented and analysed. Based on the findings, comments and conclusions on the definition of pile and raft stiffnesses for vertical and wind loading are proposed.

Keywords: design, dynamic, foundation, monitoring, pile, raft, wind load

Procedia PDF Downloads 196

Authors: N. Ismail, O. Thammajinda, U. Thongpanya

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Games-based learning (GBL) has become increasingly important in teaching and learning. This paper explains the first two phases (analysis and design) of a GBL development project, ending up with a prototype design based on students’ and teachers’ perceptions. The two phases are part of a full cycle GBL project aiming to help secondary school students in Thailand in their study of Comprehensive Sex Education (CSE). In the course of the study, we invited 1,152 students to complete questionnaires and interviewed 12 secondary school teachers in focus groups. This paper found that GBL can serve students in their learning about CSE, enabling them to gain understanding of their sexuality, develop skills, including critical thinking skills and interact with others (peers, teachers, etc.) in a safe environment. The objectives of this paper are to outline the development of GBL variables from the research question(s) into the developers’ flow chart, to be responsive to the GBL beneficiaries’ preferences and expectations, and to help in answering the research questions. This paper details the steps applied to generate GBL variables that can feed into a game flow chart to develop a GBL prototype. In our approach, we detailed two models: (1) Game Elements Model (GEM) and (2) Game Object Model (GOM). There are three outcomes of this research – first, to achieve the objectives and benefits of GBL in learning, game design has to start with the research question(s) and the challenges to be resolved as research outcomes. Second, aligning the educational aims with engaging GBL end users (students) within the data collection phase to inform the game prototype with the game variables is essential to address the answer/solution to the research question(s). Third, for efficient GBL to bridge the gap between pedagogy and technology and in order to answer the research questions via technology (i.e. GBL) and to minimise the isolation between the pedagogists “P” and technologist “T”, several meetings and discussions need to take place within the team.

Keywords: games-based learning, engagement, pedagogy, preferences, prototype

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Authors: Nikhil Padhye

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Recently a new phenomenon for bonding of polymeric films in solid-state, at ambient temperatures well below the glass transition temperature of the polymer, has been reported. This is achieved by bulk plastic compression of polymeric films held in contact. Here we analyze the process of cold-rolling of polymeric films via finite element simulations and illustrate a flexible and modular experimental rolling-apparatus that can achieve bonding of polymeric films through cold-rolling. Firstly, the classical theory of rolling a rigid-plastic thin-strip is utilized to estimate various deformation fields such as strain-rates, velocities, loads etc. in rolling the polymeric films at the specified feed-rates and desired levels of thickness-reduction(s). Predicted magnitudes of slow strain-rates, particularly at ambient temperatures during rolling, and moderate levels of plastic deformation (at which Bauschinger effect can be neglected for the particular class of polymeric materials studied here), greatly simplifies the task of material modeling and allows us to deploy a computationally efficient, yet accurate, finite deformation rate-independent elastic-plastic material behavior model (with inclusion of isotropic-hardening) for analyzing the rolling of these polymeric films. The interfacial behavior between the roller and polymer surfaces is modeled using Coulombic friction; consistent with the rate-independent behavior. The finite deformation elastic-plastic material behavior based on (i) the additive decomposition of stretching tensor (D = De + Dp, i.e. a hypoelastic formulation) with incrementally objective time integration and, (ii) multiplicative decomposition of deformation gradient (F = FeFp) into elastic and plastic parts, are programmed and carried out for cold-rolling within ABAQUS Explicit. Predictions from both the formulations, i.e., hypoelastic and multiplicative decomposition, exhibit a close match. We find that no specialized hyperlastic/visco-plastic model is required to describe the behavior of the blend of polymeric films, under the conditions described here, thereby speeding up the computation process .

Keywords: Polymer Plasticity, Bonding, Deformation Induced Mobility, Rolling

Procedia PDF Downloads 189

Authors: Khadija Refouh

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Culture bound-expressions have been a bottleneck for Natural Language Processing (NLP) and comprehension, especially in the case of machine translation (MT). In the last decade, the field of machine translation has greatly advanced. Neural machine translation NMT has recently achieved considerable development in the quality of translation that outperformed previous traditional translation systems in many language pairs. Neural machine translation NMT is an Artificial Intelligence AI and deep neural networks applied to language processing. Despite this development, there remain some serious challenges that face neural machine translation NMT when translating culture bounded-expressions, especially for low resources language pairs such as Arabic-English and Arabic-French, which is not the case with well-established language pairs such as English-French. Machine translation of opaque idioms from English into French are likely to be more accurate than translating them from English into Arabic. For example, Google Translate Application translated the sentence “What a bad weather! It runs cats and dogs.” to “يا له من طقس سيء! تمطر القطط والكلاب” into the target language Arabic which is an inaccurate literal translation. The translation of the same sentence into the target language French was “Quel mauvais temps! Il pleut des cordes.” where Google Translate Application used the accurate French corresponding idioms. This paper aims to perform NMT experiments towards better translation of opaque idioms using high quality clean multilingual corpus. This Corpus will be collected analytically from human generated idiom translation. AutoML translation, a Google Neural Machine Translation Platform, is used as a custom translation model to improve the translation of opaque idioms. The automatic evaluation of the custom model will be compared to the Google NMT using Bilingual Evaluation Understudy Score BLEU. BLEU is an algorithm for evaluating the quality of text which has been machine-translated from one natural language to another. Human evaluation is integrated to test the reliability of the Blue Score. The researcher will examine syntactical, lexical, and semantic features using Halliday's functional theory.

Keywords: multilingual corpora, natural language processing (NLP), neural machine translation (NMT), opaque idioms

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Authors: Mohsin Ejaz, Shiao-Wei Kuo

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The growing production and exploitation of fossil fuels have placed human society in serious environmental issues. As a result, it's critical to design efficient and eco-friendly energy production and storage techniques. Porous organic polymers (POPs) are multi-dimensional porous network materials developed through the formation of covalent bonds between different organic building blocks that possess distinct geometries and topologies. POPs have tunable porosities and high surface area making them a good candidate for an effective electrode material in energy storage applications. Herein, we prepared a fully benzoxazine-based porous organic polymers (TPA–DHTP–BZ POP) through sonogashira coupling of dihydroxyterephthalaldehyde (DHPT) and triphenylamine (TPA) containing benzoxazine (BZ) monomers. Firstly, both BZ monomers (TPA-BZ-Br and DHTP-BZ-Ea) were synthesized by three steps, including Schiff base, reduction, and mannich condensation reaction. Finally, the TPA–DHTP–BZ POP was prepared through the sonogashira coupling reaction of brominated monomer (TPA-BZ-Br) and ethynyl monomer (DHTP-BZ-Ea). Fourier transform infrared (FTIR) and solid-state nuclear magnetic resonance (NMR) spectroscopy confirmed the successful synthesis of monomers as well as POP. The porosity of TPA–DHTP–BZ POP was investigated by the N₂ absorption technique and showed a Brunauer–Emmett–Teller (BET) surface area of 196 m² g−¹, pore size 2.13 nm and pore volume of 0.54 cm³ g−¹, respectively. The TPA–DHTP–BZ POP experienced thermal ring-opening polymerization, resulting in poly (TPA–DHTP–BZ) POP having strong inter and intramolecular hydrogen bonds formed by phenolic groups and Mannich bridges, thereby enhancing CO₂ capture and supercapacitive performance. The poly(TPA–DHTP–BZ) POP demonstrated a remarkable CO₂ capture of 3.28 mmol g−¹ and a specific capacitance of 67 F g−¹ at 0.5 A g−¹. Thus, poly(TPA–DHTP–BZ) POP could potentially be used for energy storage and CO₂ capture applications.

Keywords: porous organic polymer, benzoxazine, sonogashira coupling, CO₂, supercapacitor

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Authors: Qin Yang, Fan Zhang, Juan Du, Chongkui Sun, Krishna Kota, Yun-Ling Zheng

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Telomeres are specialized structures at chromosome ends consisting of tandem repetitive DNA sequences [(TTAGGG)n in humans] and associated proteins, which are necessary for telomere function. Telomere lengths are tightly regulated within a narrow range in normal human somatic cells, the basis of cellular senescence and aging. Previous studies have extensively focused on how short telomeres are extended and have demonstrated that telomerase plays a central role in telomere maintenance through elongating the short telomeres. However, the molecular mechanisms of regulating excessively long telomeres are unknown. Here, we found that telomerase enzymatic component hTERT plays a dual role in the regulation of telomeres length. We analyzed single telomere alterations at each chromosomal end led to the discoveries that hTERT shortens excessively long telomeres and elongates short telomeres simultaneously, thus maintaining the optimal telomere length at each chromosomal end for an efficient protection. The hTERT-mediated telomere shortening removes large segments of telomere DNA rapidly without inducing telomere dysfunction foci or affecting cell proliferation, thus it is mechanistically distinct from rapid telomere deletion. We found that expression of hTERT generates telomeric circular DNA, suggesting that telomere homologous recombination may be involved in this telomere shortening process. Moreover, the hTERT-mediated telomere shortening is required its enzymatic activity, but telomerase RNA component hTR is not involved in it. Furthermore, shelterin protein TPP1 interacts with hTERT and recruits it on telomeres to mediate telomere shortening. In addition, telomere-associated proteins, DKC1 and TCAB1 also play roles in this process. This novel hTERT-mediated telomere shortening mechanism not only exists in cancer cells, but also in primary human cells. Thus, the hTERT-mediated telomere shortening is expected to shift the paradigm on current molecular models of telomere length maintenance, with wide-reaching consequences in cancer and aging fields.

Keywords: aging, hTERT, telomerase, telomeres, human cells

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Authors: Velimir Monty Vesselinov, Trais Kliplhuis, Hope Jasperson

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Artificial intelligence (AI) is a powerful tool in the geothermal energy sector, aiding in both exploration and utilization. Identifying promising geothermal sites can be challenging due to limited surface indicators and the need for expensive drilling to confirm subsurface resources. Geothermal reservoirs can be located deep underground and exhibit complex geological structures, making traditional exploration methods time-consuming and imprecise. AI algorithms can analyze vast datasets of geological, geophysical, and remote sensing data, including satellite imagery, seismic surveys, geochemistry, geology, etc. Machine learning algorithms can identify subtle patterns and relationships within this data, potentially revealing hidden geothermal potential in areas previously overlooked. To address these challenges, a SIML (Science-Informed Machine Learning) technology has been developed. SIML methods are different from traditional ML techniques. In both cases, the ML models are trained to predict the spatial distribution of an output (e.g., pressure, temperature, heat flux) based on a series of inputs (e.g., permeability, porosity, etc.). The traditional ML (a) relies on deep and wide neural networks (NNs) based on simple algebraic mappings to represent complex processes. In contrast, the SIML neurons incorporate complex mappings (including constitutive relationships and physics/chemistry models). This results in ML models that have a physical meaning and satisfy physics laws and constraints. The prototype of the developed software, called GeoTGO, is accessible through the cloud. Our software prototype demonstrates how different data sources can be made available for processing, executed demonstrative SIML analyses, and presents the results in a table and graphic form.

Keywords: science-informed machine learning, artificial inteligence, exploration, utilization, hidden geothermal

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Authors: Dong Seop Lee, Byung Sik Kim

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In the 4th Industrial Revolution, various intelligent technologies have been developed in many fields. These artificial intelligence technologies are applied in various services, including disaster management. Disaster information management does not just support disaster work, but it is also the foundation of smart disaster management. Furthermore, it gets historical disaster information using artificial intelligence technology. Disaster information is one of important elements of entire disaster cycle. Disaster information management refers to the act of managing and processing electronic data about disaster cycle from its’ occurrence to progress, response, and plan. However, information about status control, response, recovery from natural and social disaster events, etc. is mainly managed in the structured and unstructured form of reports. Those exist as handouts or hard-copies of reports. Such unstructured form of data is often lost or destroyed due to inefficient management. It is necessary to manage unstructured data for disaster information. In this paper, the Optical Character Recognition approach is used to convert handout, hard-copies, images or reports, which is printed or generated by scanners, etc. into electronic documents. Following that, the converted disaster data is organized into the disaster code system as disaster information. Those data are stored in the disaster database system. Gathering and creating disaster information based on Optical Character Recognition for unstructured data is important element as realm of the smart disaster management. In this paper, Korean characters were improved to over 90% character recognition rate by using upgraded OCR. In the case of character recognition, the recognition rate depends on the fonts, size, and special symbols of character. We improved it through the machine learning algorithm. These converted structured data is managed in a standardized disaster information form connected with the disaster code system. The disaster code system is covered that the structured information is stored and retrieve on entire disaster cycle such as historical disaster progress, damages, response, and recovery. The expected effect of this research will be able to apply it to smart disaster management and decision making by combining artificial intelligence technologies and historical big data.

Keywords: disaster information management, unstructured data, optical character recognition, machine learning

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Authors: Xiyue Wang, Shaoning Yan

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Cities in China today is faced with an increasingly serious river ecological crisis accompanying with the development of urbanization: waterlogging on account of the fragmented urban natural hydrological system; the limited ecological function of the hydrological system caused by a destruction of water system and waterfront ecological environment. Additionally, the eco-hydrological processes of rivers are affected by various environmental factors, which are more complex in the context of urban environment. Therefore, efficient hydrological monitoring and analysis tools, accurate and visual hydrological and hydraulic models are becoming more important basis for decision-makers and an important way for landscape architects to solve urban hydrological problems, formulating sustainable and forward-looking schemes. The study mainly introduces the river and flood analysis model based on the platform of Autodesk Civil 3D. Taking the Luanhe River in Qian'an City of Hebei Province as an example, the 3D models of the landform, river, embankment, shoal, pond, underground stream and other land features were initially built, with which the water transfer simulation analysis, river floodplain analysis, and river ecology analysis were carried out, ultimately the real-time visualized simulation and analysis of rivers in various hypothetical scenarios were realized. Through the establishment of digital hydrological and hydraulic model, the hydraulic data can be accurately and intuitively simulated, which provides basis for rational water system and benign urban ecological system design. Though, the hydrological and hydraulic model based on Autodesk Civil3D own its boundedness: the interaction between the model and other data and software is unfavorable; the huge amount of 3D data and the lack of basic data restrict the accuracy and application range. The hydrological and hydraulic model based on Autodesk Civil3D platform provides more possibility to access convenient and intelligent tool for urban planning and monitoring, a solid basis for further urban research and design.

Keywords: visualization, hydrological and hydraulic model, Autodesk Civil 3D, urban river

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Authors: Selin Tosun

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The improvements in technology have started to transform the way we live, work, play, and commute in our cities. The emerging smart city understanding has been paving the way for more efficient, more useful, and more profitable cities. Smart sensors, smart lighting, smart waste, water and electricity management, smart transportation and communication systems are introduced to cities at a rapid pace. In today's world, innovation is often correlated with start-up companies and technological pioneers seeking broader economic objectives such as production and competitiveness. The government's position is primarily that of an enabler, with creativity mostly coming from the private sector. The paper argues that to achieve sustainable development, the ways in which smart and sustainable city approaches are being applied to cities need to be redefined. The research aims to address common discussions in the discourse of smart and sustainable cities criticizing the priority of lifestyle sterilization over human-centered sustainable interventions and social innovation strategies. The dichotomy between the fact that smart cities are mostly motivated by the competitive global market and the fact that the delocalization is, in fact, their biggest problem in the way of becoming authentic, sustainable cities is the main challenge that we face today. In other words, the key actors in smart cities have different and somewhat conflicting interests and demands. By reviewing the roles of the public and private actors in smart city making, the paper aspires to reconceptualize the understanding of “smartness” in achieving sustainable development in which the “smartness” is understood as a multi-layered complex phenomenon that can be channeled through different dynamics. The case cities around the world are explored and compared in terms of their technological innovations, governance and policy innovations, public-private stakeholder relationships, and the understanding of the public realm. The study aims to understand the current trends and general dynamics in the field, key issues that are being addressed, the scale that is preferred to reflect upon and the projects that are designed for the particular issues.

Keywords: smart city, sustainable development, technological innovation, social innovation

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Authors: Shaibu Baanni Azumah, William Adzawla

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Rice is an important staple crop, with current demand higher than the domestic supply in Ghana. This has led to a high and unfavourable import bill. Therefore, recent policies and interventions in the agricultural sub-sector aim at promoting various improved agricultural technologies in order to improve domestic production and reduce the importation of rice. In this study, we examined the effect of the adoption of Urea Deep Placement (UDP) technology by rice farmers on the position of the production frontier. This involved 200 farmers selected through a multi stage sampling technique in the Northern region of Ghana. A Cobb-Douglas stochastic frontier model was fitted. The result showed that the adoption of UDP technology shifts the output frontier outward and also move the farmers closer to the frontier. Farmers were also operating under diminishing returns to scale which calls for redress. Other factors that significantly influenced rice production were farm size, labour, use of certified seeds and NPK fertilizer. Although there was an opportunity for improvement, the farmers were highly efficient (92%), compared to previous studies. Farmers’ efficiency was improved through increased education, household size, experience, access to credit, and lack of extension service provision by MoFA. The study recommends the revision of Ghana’s agricultural policy to include the UDP technology. Agricultural Extension officers of the Ministry of Food and Agriculture (MoFA) should be trained on the UDP technology to support IFDC’s drive to improve adoption by rice farmers. Rice farmers are also encouraged to expand their farm lands, improve plant population, and also increase the usage of fertilizer to improve yields. Mechanisms through which credit can be made easily accessible and effectively utilised should be identified and promoted.

Keywords: efficiency, rice farmers, stochastic frontier, UDP technology

Procedia PDF Downloads 409

Authors: Amirreza Fazely Hamedani, Muzzamil Aziz, Philipp Wieder, Ramin Yahyapour

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Software-defined networking in recent years came into the sight of so many network designers as a successor to the traditional networking. Unlike traditional networks where control and data planes engage together within a single device in the network infrastructure such as switches and routers, the two planes are kept separated in software-defined networks (SDNs). All critical decisions about packet routing are made on the network controller, and the data level devices forward the packets based on these decisions. This type of network is vulnerable to DDoS attacks, degrading the overall functioning and performance of the network by continuously injecting the fake flows into it. This increases substantial burden on the controller side, and the result ultimately leads to the inaccessibility of the controller and the lack of network service to the legitimate users. Thus, the protection of this novel network architecture against denial of service attacks is essential. In the world of cybersecurity, attacks and new threats emerge every day. It is essential to have tools capable of managing and analyzing all this new information to detect possible attacks in real-time. These tools should provide a comprehensive solution to automatically detect, predict and prevent abnormalities in the network. Big data encompasses a wide range of studies, but it mainly refers to the massive amounts of structured and unstructured data that organizations deal with on a regular basis. On the other hand, it regards not only the volume of the data; but also that how data-driven information can be used to enhance decision-making processes, security, and the overall efficiency of a business. This paper presents an intelligent big data framework as a solution to handle illegitimate traffic burden on the SDN network created by the numerous DDoS attacks. The framework entails an efficient defence and monitoring mechanism against DDoS attacks by employing the state of the art machine learning techniques.

Keywords: apache spark, apache kafka, big data, DDoS attack, machine learning, SDN network

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Authors: Akram Abi, Clarissa Müller, Hans-Joachim Jördening

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Laminaribiose is a beta-1,3-glycoside which is used in the medical field for the treatment of dermatitis and also can be used as a building block for new pharmaceutics. The conventional process of laminaribiose production is the uneconomical process of hydrolysis of laminarin extracted from natural polysaccharides of plant origin. A more economical approach however is attainable by enzymatically synthesis of laminaribiose via a reverse phosphorylase reaction catalyzed by laminaribiose phosphorylase (LP) from Euglena gracilis. Different cultivation methods of Euglena gracilis and the effect on LP production have been investigated. Buffered/unbuffered heterotrophic and mixotrophic cultivations of Euglena gracilis has been carried out. Changes of biomass and LP production, glucose level and pH, cell count and shape has been monitored in the course of time. The results obtained from experiments each in three repetitions, show that in the heterotrophic cultivation of Euglena gracilis not only more biomass is produced compared to mixotrophic cultivation, but also higher specific protein concentration is achieved. Furthermore, the LP activity test showed that the protein extracted from heterotrophically cultured cells has a higher LP activity. It was also observed that the cells develop in a distinctive different shape between these two cultures and have different length to width ratios. Taking the heterotrophic culture as the more efficient cultivation method in LP production, another comparative experiment between buffered and unbuffered heterothrophic culture was carried out that showed the unbuffered culture has advantages over the other one in respect of both LP production and resulting activity. A hetrotrophic cultivation of Euglena gracilis in a 5L bioreactor with controlled operating conditions showed a distinctive improvement of all the aspects of culture compared to the shaking flask cultivations. Biomass production was improved from 5 to more than 8 g/l (dry weight) which resulted in a specific protein concentration of 45 g/l in the heterotrophic cultivation in the bioreactor. In further attempts to improve LP production, different purification methods were tested and each method was checks through an activity assay. A laminaribiose yield of 35% was achieved which was by far the highest amount amongst different methods tested.

Keywords: euglena gracilis, heterotrophic culture, laminaribiose production, mixotrophic culture

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Authors: Ariba Siddiqui, Amber Khan

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The need for bio-implantable devices in the field of medical sciences has been increasing day by day; however, the charging of these devices is a major issue. Batteries, a very common method of powering the implants, have a limited lifetime and bulky nature. Therefore, as a replacement of batteries, acoustic power transfer (APT) technology is being accepted as the most suitable technique to wirelessly power the medical implants in the present scenario. The basic model of APT consists of piezoelectric transducers that work on the principle of converse piezoelectric effect at the transmitting end and direct piezoelectric effect at the receiving end. This paper provides mechanistic insight into the parameters affecting the design and efficient working of acoustic power transfer systems. The optimum design considerations have been presented that will help to compress the size of the device and augment the intensity of the pressure wave. A COMSOL model of the PZT (Lead Zirconate Titanate) transducer was developed. The model was simulated and analyzed on a frequency spectrum. The simulation results displayed that the efficiency of these devices is strongly dependent on the frequency of operation, and a wrong choice of the operating frequency leads to the high absorption of acoustic field inside the tissue (medium), poor power strength, and heavy transducers, which in effect influence the overall configuration of the acoustic systems. Considering all the tradeoffs, the simulations were performed again by determining an optimum frequency (900 kHz) that resulted in the reduction of the transducer's thickness to 1.96 mm and augmented the power strength with an intensity of 432 W/m². Thus, the results obtained after the second simulation contribute to lesser attenuation, lightweight systems, high power intensity, and also comply with safety limits provided by the U.S Food and Drug Administration (FDA). It was also found that the chosen operating frequency enhances the directivity of the acoustic wave at the receiver side.

Keywords: acoustic power, bio-implantable, COMSOL, Lead Zirconate Titanate, piezoelectric, transducer

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Authors: Hatem Abou-Senna

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Interstate 4 (I-4) is a primary east-west transportation corridor between Tampa and Daytona cities, serving commuters, commercial and recreational traffic. I-4 is known to have severe recurring congestion during peak hours. The congestion spans about 11 miles in the evening peak period in the central corridor area as it is considered the only non-tolled limited access facility connecting the Orlando Central Business District (CBD) and the tourist attractions area (Walt Disney World). Florida officials had been skeptical of tolling I-4 prior to the recent legislation, and the public through the media had been complaining about the excessive toll facilities in Central Florida. So, in search for plausible mitigation to the congestion on the I-4 corridor, this research is implemented to evaluate the effectiveness of different toll pricing alternatives that might divert traffic from I-4 to the toll facilities during the peak period. The network is composed of two main diverging limited access highways, freeway (I-4) and toll road (SR 417) in addition to two east-west parallel toll roads SR 408 and SR 528, intersecting the above-mentioned highways from both ends. I-4 and toll road SR 408 are the most frequently used route by commuters. SR-417 is a relatively uncongested toll road with 15 miles longer than I-4 and $5 tolls compared to no monetary cost on 1-4 for the same trip. The results of the calibrated Orlando PARAMICS network showed that percentages of route diversion vary from one route to another and depends primarily on the travel cost between specific origin-destination (O-D) pairs. Most drivers going from Disney (O1) or Lake Buena Vista (O2) to Lake Mary (D1) were found to have a high propensity towards using I-4, even when eliminating tolls and/or providing real-time information. However, a diversion from I-4 to SR 417 for these OD pairs occurred only in the cases of the incident and lane closure on I-4, due to the increase in delay and travel costs, and when information is provided to travelers. Furthermore, drivers that diverted from I-4 to SR 417 and SR 528 did not gain significant travel-time savings. This was attributed to the limited extra capacity of the alternative routes in the peak period and the longer traveling distance. When the remaining origin-destination pairs were analyzed, average travel time savings on I-4 ranged between 10 and 16% amounting to 10 minutes at the most with a 10% increase in the network average speed. High propensity of diversion on the network increased significantly when eliminating tolls on SR 417 and SR 528 while doubling the tolls on SR 408 along with the incident and lane closure scenarios on I-4 and with real-time information provided. The toll roads were found to be a viable alternative to I-4 for these specific OD pairs depending on the user perception of the toll cost which was reflected in their specific travel times. However, on the macroscopic level, it was concluded that route diversion through toll reduction or elimination on surrounding toll roads would only have a minimum impact on reducing I-4 congestion during the peak period.

Keywords: congestion pricing, dynamic feedback assignment, microsimulation, paramics, route diversion

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Authors: W. Jahouach-Rabai, J. Aribi, Z. Azzouz-Berriche, R. Lahsni, F. Hosni

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Gamma irradiation applied in removing pharmaceutical contaminants from wastewater is an effective advanced oxidation process (AOP), considered as an alternative to conventional water treatment technologies. In this purpose, the degradation efficiency of several detected contaminants under gamma irradiation was evaluated. In fact, radiolysis of organic pollutants in aqueous solutions produces powerful reactive species, essentially hydroxyl radical ( ·OH), able to destroy recalcitrant pollutants in water. Pharmaceuticals considered in this study are aqueous solutions of paracetamol, ibuprofen, and diclofenac at different concentrations 0.1-1 mmol/L, which were treated with irradiation doses from 3 to 15 kGy. The catalytic oxidation of these compounds by gamma irradiation was investigated using hydrogen peroxide (H₂O₂) as a convenient oxidant. Optimization of the main parameters influencing irradiation process, namely irradiation doses, initial concentration and oxidant volume (H₂O₂) were investigated, in the aim to release high degradation efficiency of considered pharmaceuticals. Significant modifications attributed to these parameters appeared in the variation of degradation efficiency, chemical oxygen demand removal (COD) and concentration of radio-induced radicals, confirming them synergistic effect to attempt total mineralization. Pseudo-first-order reaction kinetics could be used to depict the degradation process of these compounds. A sophisticated analytical study was released to quantify the detected radio-induced radicals (electron paramagnetic resonance spectroscopy (EPR) and high performance liquid chromatography (HPLC)). All results showed that this process is effective for the degradation of many pharmaceutical products in aqueous solutions due to strong oxidative properties of generated radicals mainly hydroxyl radical. Furthermore, the addition of an optimal amount of H₂O₂ was efficient to improve the oxidative degradation and contribute to the high performance of this process at very low doses (0.5 and 1 kGy).

Keywords: AOP, COD, hydroxyl radical, EPR, gamma irradiation, HPLC, pharmaceuticals

Procedia PDF Downloads 170

Authors: Tingting Huang, Sha Li, Geoffrey Griffiths, Martin Lukac, Jianning Zhu

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Territorial landscapes have experienced a gradual loss of their typical characteristics during long-term human activities. In order to protect the integrity of regional landscapes, it is necessary to characterize, evaluate and protect them in a graded manner. The study takes Fujian, China, as an example and classifies the landscape characters of the site at the regional scale, middle scale, and detailed scale. A multi-scale approach combining parametric and holistic approaches is used to classify and partition the landscape character types (LCTs) and landscape character areas (LCAs) at different scales, and a multi-element landscape assessment approach is adopted to explore the conservation strategies of the landscape character. Firstly, multiple fields and multiple elements of geography, nature and humanities were selected as the basis of assessment according to the scales. Secondly, the study takes a parametric approach to the classification and partitioning of landscape character, Principal Component Analysis, and two-stage cluster analysis (K-means and GMM) in MATLAB software to obtain LCTs, combines with Canny Operator Edge Detection Algorithm to obtain landscape character contours and corrects LCTs and LCAs by field survey and manual identification methods. Finally, the study adopts the Landscape Sensitivity Assessment method to perform landscape character conservation analysis and formulates five strategies for different LCAs: conservation, enhancement, restoration, creation, and combination. This multi-scale identification approach can efficiently integrate multiple types of landscape character elements, reduce the difficulty of broad-scale operations in the process of landscape character conservation, and provide a basis for landscape character conservation strategies. Based on the natural background and the restoration of regional characteristics, the results of landscape character assessment are scientific and objective and can provide a strong reference in regional and national scale territorial spatial planning.

Keywords: parameterization, multi-scale, landscape character identify, landscape character assessment

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Authors: Jaruwan Chutrtong, Tanakwan Bussabun

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Study production of tempeh inoculums powder by freeze-drying comparison with dry at 50°C and the sun bask for developing efficient tempeh inoculums for tempeh producing. Rhizopus oligosporus in PDA slant cultures was incubated at 30°C for 3-5 days until spores and mycelium. Preparation spores suspension with sterilized water and then count the number of started spores. Fill spores suspension in Rice flour and soy flour, mixed with water (in the ratio 10: 7), which is steamed and sterilized at 121°C 15min. Incubated at room temperature for 4 days, count number of spores. Then take the progressive infection and full spore dough to dry at 50°C, sun bask, and lyophilize. Grind to powder. Then pack in plastic bags, stored at 5°C. To investigate quality of inoculums which use different methods, tempeh was fermented every 4 weeks for 24 weeks of the experiment. The result found that rice flour is not suitable to use as raw material in the production of powdered spores. Fungi can growth rarely. Less number of spores and requires more time than soy flour. For drying method, lyophilization is the least possible time. Samples from this method are very hard and very dark and harder to grind than other methods. Drying at 50°C takes longer time than lyophilization but can also set time use for drying. Character of the dry samples is hard solid and brown color, but can be grinded easier. The sun drying takes the longest time, can’t determine the exact time. When the spore powder was used to fermented tempeh immediately, product has similar characters as which use spores that was fresh prepared. The tempeh has normal quality. When spore powder stored at low temperature, tempeh from storage spore in weeks 4, 8 and 12 is still normal. Time spending in production was close to the production of fresh spores. After storage spores for 16 and 20 weeks, tempeh is still normal but growth and sporulation were take longer time than usual (about 6 hours). At 24 week storage, fungal growth is not good, made tempeh looks inferior to normal color, also smell and texture.

Keywords: freez drying, preparation, spores powder, tempeh

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Authors: Ravi Kant, Banshi D. Gupta

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The unbridled application of insecticides to enhance agricultural yield has become a matter of grave concern to both the environment and the human health and, thus pose a potential threat to sustainable development. Fenitrothion is an extensively used organophosphate insecticide whose residues are reported to be extremely toxic for birds, humans and aquatic life. A sensitive, swift and accurate detection protocol for fenitrothion is, thus, highly demanded. In this work, we report an SPR based fiber optic sensor for the detection of fenitrothion, where a nanocomposite arrangement of Ta2O5 and reduced graphene oxide (rGO) (Ta₂O₅: rGO) decorated on silver coated unclad core region of an optical fiber forms the sensing channel. A nanocomposite arrangement synergistically integrates the properties of involved components and consequently furnishes a conducive framework for sensing applications. The modification of the dielectric function of the sensing layer on exposure to fenitrothion solutions of diverse concentration forms the sensing mechanism. This modification is reflected in terms of the shift in resonance wavelength. Experimental variables such as the concentration of rGO in the nanocomposite configuration, dip time of silver coated fiber optic probe for deposition of sensing layer and influence of pH on the performance of the sensor have been optimized to extract the best performance of the sensor. SPR studies on the optimized sensing probe reveal the high sensitivity, wide operating range and good reproducibility of the fabricated sensor, which unveil the promising utility of Ta₂O₅: rGO nanocomposite framework for developing an efficient detection methodology for fenitrothion. FOSPR approach in cooperation with nanomaterials projects the present work as a beneficial approach for fenitrothion detection by imparting numerous useful advantages such as sensitivity, selectivity, compactness and cost-effectiveness.

Keywords: surface plasmon resonance, optical fiber, sensor, fenitrothion

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Authors: Santimoy Khilari, Debabrata Pradhan

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Ever−increasing energy demand and growing energy crisis along with environmental issues emphasize the research on sustainable energy conversion and storage systems. Recently, supercapacitors or electrochemical capacitors emerge as a promising energy storage technology for future generation. The activity of supercapacitors generally depends on the efficiency of its electrode materials. So, the development of cost−effective efficient electrode materials for supercapacitors is one of the challenges to the scientific community. Transition metal oxides with spinel crystal structure receive much attention for different electrochemical applications in energy storage/conversion devices because of their improved performance as compared to simple oxides. In the present study, we have synthesized polypyrrole (PPy) supported manganese cobaltite nanorods (MnCo2O4 NRs) hybrid electrode material for supercapacitor application. The MnCo2O4 NRs were synthesized by a simple hydrothermal and calcination approach. The MnCo2O4 NRs/PPy hybrid was prepared by in situ impregnation of MnCo2O4 NRs during polymerization of pyrrole. The surface morphology and microstructure of as−synthesized samples was characterized by scanning electron microscopy and transmission electron microscopy, respectively. The crystallographic phase of MnCo2O4 NRs, PPy and hybrid was determined by X-ray diffraction. Electrochemical charge storage activity of MnCo2O4 NRs, PPy and MnCo2O4 NRs/PPy hybrid was evaluated from cyclic voltammetry, chronopotentiometry and electrochemical impedance spectroscopy. Significant improvement of specific capacitance was achieved in MnCo2O4 NRs/PPy hybrid as compared to the individual components. Furthermore, the mechanically mixed MnCo2O4 NRs, and PPy shows lower specific capacitance as compared to MnCo2O4 NRs/PPy hybrid suggesting the importance of in situ hybrid preparation. The stability of as prepared electrode materials was tested by cyclic charge-discharge measurement for 1000 cycles. Maximum 94% capacitance was retained with MnCo2O4 NRs/PPy hybrid electrode. This study suggests that MnCo2O4 NRs/PPy hybrid can be used as a low cost electrode material for charge storage in supercapacitors.

Keywords: supercapacitors, nanorods, spinel, MnCo2O4, polypyrrole

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Authors: Manpreet Kaur, Cheol-Hong Min

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The increasing advancement in the robotics equipped with machine vision sensors applied to precision agriculture is a demanding solution for various problems in the agricultural farms. An important issue related with the machine vision system concerns crop row and weed detection. This paper proposes an automatic furrow detection system based on real-time processing for identifying crop rows in maize fields in the presence of weed. This vision system is designed to be installed on the farming vehicles, that is, submitted to gyros, vibration and other undesired movements. The images are captured under image perspective, being affected by above undesired effects. The goal is to identify crop rows for vehicle navigation which includes weed removal, where weeds are identified as plants outside the crop rows. The images quality is affected by different lighting conditions and gaps along the crop rows due to lack of germination and wrong plantation. The proposed image processing method consists of four different processes. First, image segmentation based on HSV (Hue, Saturation, Value) decision tree. The proposed algorithm used HSV color space to discriminate crops, weeds and soil. The region of interest is defined by filtering each of the HSV channels between maximum and minimum threshold values. Then the noises in the images were eliminated by the means of hybrid median filter. Further, mathematical morphological processes, i.e., erosion to remove smaller objects followed by dilation to gradually enlarge the boundaries of regions of foreground pixels was applied. It enhances the image contrast. To accurately detect the position of crop rows, the region of interest is defined by creating a binary mask. The edge detection and Hough transform were applied to detect lines represented in polar coordinates and furrow directions as accumulations on the angle axis in the Hough space. The experimental results show that the method is effective.

Keywords: furrow detection, morphological, HSV, Hough transform

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Authors: Yoftahe Nigussie

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This research explores the implementation of a vapor absorption refrigeration system (VARS) in mini-bus cars to enhance air conditioning efficiency. The conventional vapor compression refrigeration system (VCRS) in vehicles relies on mechanical work from the engine, leading to increased fuel consumption. The proposed VARS aims to utilize waste heat and exhaust gas from the internal combustion engine to cool the mini-bus cabin, thereby reducing fuel consumption and atmospheric pollution. The project involves two models: Model 1, a two-fluid vapor absorption system (VAS), and Model 2, a three-fluid VAS. Model 1 uses ammonia (NH₃) and water (H₂O) as refrigerants, where water absorbs ammonia rapidly, producing a cooling effect. The absorption cycle operates on the principle that absorbing ammonia in water decreases vapor pressure. The ammonia-water solution undergoes cycles of desorption, condensation, expansion, and absorption, facilitated by a generator, condenser, expansion valve, and absorber. The objectives of this research include reducing atmospheric pollution, minimizing air conditioning maintenance costs, lowering capital costs, enhancing fuel economy, and eliminating the need for a compressor. The comparison between vapor absorption and compression systems reveals advantages such as smoother operation, fewer moving parts, and the ability to work at lower evaporator pressures without affecting the Coefficient of Performance (COP). The proposed VARS demonstrates potential benefits for mini-bus air conditioning systems, providing a sustainable and energy-efficient alternative. By utilizing waste heat and exhaust gas, this system contributes to environmental preservation while addressing economic considerations for vehicle owners. Further research and development in this area could lead to the widespread adoption of vapor absorption technology in automotive air conditioning systems.

Keywords: room, zone, space, thermal resistance

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Authors: Auwal Abubakar, Shazril Imran Shaukat, Noor Khairiah A. Karim, Mohammed Zakir Kassim, Gokula Kumar Appalanaido, Hafiz Mohd Zin

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Background: Voluntary deep inspiration breath-hold radiotherapy (vDIBH-RT) is an effective cardiac dose reduction technique during left breast radiotherapy. This study aimed to assess the accuracy of the implementation of the vDIBH technique among left breast cancer patients without the use of a special device such as a surface-guided imaging system. Methods: The vDIBH-RT technique was implemented among thirteen (13) left breast cancer patients at the Advanced Medical and Dental Institute (AMDI), Universiti Sains Malaysia. Breath-hold monitoring was performed based on breath-hold skin marks and laser light congruence observed on zoomed CCTV images from the control console during each delivery. The initial setup was verified using cone beam computed tomography (CBCT) during breath-hold. Each field was delivered using multiple beam segments to allow a delivery time of 20 seconds, which can be tolerated by patients in breath-hold. The data were analysed using an in-house developed MATLAB algorithm. PTV margin was computed based on van Herk's margin recipe. Results: The setup error analysed from CBCT shows that the population systematic error in lateral (x), longitudinal (y), and vertical (z) axes was 2.28 mm, 3.35 mm, and 3.10 mm, respectively. Based on the CBCT image guidance, the Planning target volume (PTV) margin that would be required for vDIBH-RT using CCTV/Laser monitoring technique is 7.77 mm, 10.85 mm, and 10.93 mm in x, y, and z axes, respectively. Conclusion: It is feasible to safely implement vDIBH-RT among left breast cancer patients without special equipment. The breath-hold monitoring technique is cost-effective, radiation-free, easy to implement, and allows real-time breath-hold monitoring.

Keywords: vDIBH, cone beam computed tomography, radiotherapy, left breast cancer

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Authors: Rodrigo Arnaldo Scarpel

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Trend following is a widely accepted investment strategy that adopts a rule-based trading mechanism that rather than striving to predict market direction or on information gathering to decide when to buy and when to sell a stock. Thus, in trend following one must respond to market’s movements that has recently happen and what is currently happening, rather than on what will happen. Optimally, in trend following strategy, is to catch a bull market at its early stage, ride the trend, and liquidate the position at the first evidence of the subsequent bear market. For applying the trend following strategy one needs to find the trend and identify trade signals. In order to avoid false signals, i.e., identify fluctuations of short, mid and long terms and to separate noise from real changes in the trend, most academic works rely on moving averages and other technical analysis indicators, such as the moving average convergence divergence (MACD) and the relative strength index (RSI) to uncover intelligible stock trading rules following trend following strategy philosophy. Recently, some works has applied machine learning techniques for trade rules discovery. In those works, the process of rule construction is based on evolutionary learning which aims to adapt the rules to the current environment and searches for the global optimum rules in the search space. In this work, instead of focusing on the usage of machine learning techniques for creating trading rules, a time series trend classification employing a semi-supervised approach was used to early identify both the beginning and the end of upward and downward trends. Such classification model can be employed to identify trade signals and the decision-making procedure is that if an up-trend (down-trend) is identified, a buy (sell) signal is generated. Semi-supervised learning is used for model training when only part of the data is labeled and Semi-supervised classification aims to train a classifier from both the labeled and unlabeled data, such that it is better than the supervised classifier trained only on the labeled data. For illustrating the proposed approach, it was employed daily trade information, including the open, high, low and closing values and volume from January 1, 2000 to December 31, 2022, of the São Paulo Exchange Composite index (IBOVESPA). Through this time period it was visually identified consistent changes in price, upwards or downwards, for assigning labels and leaving the rest of the days (when there is not a consistent change in price) unlabeled. For training the classification model, a pseudo-label semi-supervised learning strategy was used employing different technical analysis indicators. In this learning strategy, the core is to use unlabeled data to generate a pseudo-label for supervised training. For evaluating the achieved results, it was considered the annualized return and excess return, the Sortino and the Sharpe indicators. Through the evaluated time period, the obtained results were very consistent and can be considered promising for generating the intended trading signals.

Keywords: evolutionary learning, semi-supervised classification, time series data, trading signals generation

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Authors: Saad M. Algharib

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The major concern of planners when placing fire stations is finding their optimal locations such that the fire companies can reach fire locations within reasonable response time or distance. Planners are also concerned with the numbers of fire stations that are needed to cover all service areas and the fires, as demands, with standard response time or distance. One of the tools for such analysis is location-allocation models. Location-allocation models enable planners to determine the optimal locations of facilities in an area in order to serve regional demands in the most efficient way. The purpose of this study is to examine the geographic distribution of the existing fire stations in Kuwait City. This study utilized location-allocation models within the Geographic Information System (GIS) environment and a number of statistical functions to assess the current locations of fire stations in Kuwait City. Further, this study investigated how well all service areas are covered and how many and where additional fire stations are needed. Four different location-allocation models were compared to find which models cover more demands than the others, given the same number of fire stations. This study tests many ways to combine variables instead of using one variable at a time when applying these models in order to create a new measurement that influences the optimal locations for locating fire stations. This study also tests how location-allocation models are sensitive to different levels of spatial dependency. The results indicate that there are some districts in Kuwait City that are not covered by the existing fire stations. These uncovered districts are clustered together. This study also identifies where to locate the new fire stations. This study provides users of these models a new variable that can assist them to select the best locations for fire stations. The results include information about how the location-allocation models behave in response to different levels of spatial dependency of demands. The results show that these models perform better with clustered demands. From the additional analysis carried out in this study, it can be concluded that these models applied differently at different spatial patterns.

Keywords: geographic information science, GIS, location-allocation models, geography

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Authors: Sheikh Mohammed Rafiul Huque

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Bangladesh since its inception has been an economy that is fuelled by agriculture and agriculture has significant contribution to the GDP of Bangladesh. The agriculture of Bangladesh predominantly and historically dependent on organic sources of raw material though the place has taken in decades by inorganic sources of raw materials due to the high demand of food for rapidly growing of population. Meanwhile, a new market segment, which is niche market, has been evolving in the urban area in favor of organic products, though 71.1% population living in rural areas is dependent mainly on conventional products. The new market segment is search of healthy and safer source of food and they could believe that organic products are the solution of that. In Bangladesh, food adulteration is very common practices among the shop-keepers to extend the shelf life of raw vegetables and fruits. The niche group of city dwellers is aware about the fact and gradually shifting their buying behavior to organic products. A recent survey on organic farming revealed that 16,200 hectares under organic farming in recent time, which was only 2,500 hectares in 2008. This study is focused on consumer awareness of organic products and tried to explore the factors affecting organic food consumption among high income group of people. The hypothesis is developed to explore the effect of gender (GENDER), ability to purchase (ABILITY) and health awareness (HEALTH) on purchase intention (INTENTION). A snowball sampling was administered among the high income group of people in Dhaka city among 150 respondents. In this sampling process the study could identify only those samples who has consume organic products. A Partial Least Square (PLS) method was used to analyze data using path analysis. It was revealed from the analysis that coefficient determination R2 is 0.829 for INTENTION endogenous latent variable. This means that three latent variables (GENDER, ABILITY, and HEALTH) significantly explain 82.9% of the variance in INTENTION of purchasing organic products. Moreover, GENDER solely explains 6.3% and 8.6% variability of ABILITY and HEALTH respectively. The inner model suggests that HEALTH has strongest negative effect on INTENTION (-0.647) followed by ABILITY (0.344) and GENDER (0.246). The hypothesized path relationship between ABILITY->INTENTION, HEALTH->INTENTION and GENDER->INTENTION are statistically significant. Furthermore, the hypothesized path relationship between GENDER->ABILITY (0.262) and GENDER->HEALTH (-0.292) also statistically significant. The purpose of the study is to demonstrate how an organic product producer can improve his participatory guarantee system (PGS) while marketing the products. The study focuses on understanding gender (GENDER), ability (ABILITY) and health (HEALTH) factors while positioning the products (INTENTION) in the mind of the consumer. In this study, the respondents are found to care about high price and ability to purchase variables with loading -0.920 and 0.898. They are good indicators of ability to purchase (ABILITY). The marketers should consider about price of organic comparing to conventional products while marketing, otherwise, that will create negative intention to buy with a loading of -0.939. Meanwhile, it is also revealed that believability of chemical free component in organic products and health awareness affects health (HEALTH) components with high loading -0.941 and 0.682. The study analyzes that low believability of chemical free component and high price of organic products affects intension to buy. The marketers should not overlook this point while targeting the consumers in Bangladesh.

Keywords: health awareness, organic products, purchase ability, purchase intention

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Authors: L. Marcos Domínguez, Nils Rage, Ongun B. Kazanci, Bjarne W. Olesen

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Thermally Activated Building Systems (TABS) have proven to be an energy-efficient solution to provide buildings with an optimal indoor thermal environment. This solution uses the structure of the building to store heat, reduce the peak loads, and decrease the primary energy demand. TABS require the heated or cooled surfaces to be as exposed as possible to the indoor space, but exposing the bare concrete surfaces has a diminishing effect on the acoustic qualities of the spaces in a building. Acoustic solutions capable of providing optimal acoustic comfort and allowing the heat exchange between the TABS and the room are desirable. In this study, the effects of free-hanging units on the cooling performance of TABS and the occupants’ thermal comfort was measured in a full-scale TABS laboratory. Investigations demonstrate that the use of free-hanging sound absorbers are compatible with the performance of TABS and the occupant’s thermal comfort, but an appropriate acoustic design is needed to find the most suitable solution for each case. The results show a reduction of 11% of the cooling performance of the TABS when 43% of the ceiling area is covered with free-hanging horizontal sound absorbers, of 23% for 60% ceiling coverage ratio and of 36% for 80% coverage. Measurements in actual buildings showed an increase of the room operative temperature of 0.3 K when 50% of the ceiling surface is covered with horizontal panels and of 0.8 to 1 K for a 70% coverage ratio. According to numerical simulations using a new TRNSYS Type, the use of comfort ventilation has a considerable influence on the thermal conditions in the room; if the ventilation is removed, then the operative temperature increases by 1.8 K for a 60%-covered ceiling.

Keywords: acoustic comfort, concrete core activation, full-scale measurements, thermally activated building systems, TRNSys

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Authors: Kottaridi Klimentia, Demopoulos Vasilis, Sidiropoulos Anastasios, Ihara Diego, Nikolaidis Vasileios, Antonopoulos Dimitrios

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Aflatoxins are highly poisonous and carcinogenic compounds produced by species of the genus Aspergillus spp. that can infect a variety of agricultural foods, including dried figs. Biological and environmental factors, such as population, pathogenicity, and aflatoxinogenic capacity of the strains, topography, soil, and climate parameters of the fig orchards, are believed to have a strong effect on aflatoxin levels. Existing methods for aflatoxin detection and measurement, such as high performance liquid chromatography (HPLC), and enzyme-linked immunosorbent assay (ELISA), can provide accurate results, but the procedures are usually time-consuming, sample-destructive, and expensive. Predicting aflatoxin levels prior to crop harvest is useful for minimizing the health and financial impact of a contaminated crop. Consequently, there is interest in developing a tool that predicts aflatoxin levels based on topography and soil analysis data of fig orchards. This paper describes the development of a risk assessment tool for the contamination of aflatoxin on dried figs, based on the location and altitude of the fig orchards, the population of the fungus Aspergillus spp. in the soil, and soil parameters such as pH, saturation percentage (SP), electrical conductivity (EC), organic matter, particle size analysis (sand, silt, clay), the concentration of the exchangeable cations (Ca, Mg, K, Na), extractable P, and trace of elements (B, Fe, Mn, Zn and Cu), by employing machine learning methods. In particular, our proposed method integrates three machine learning techniques, i.e., dimensionality reduction on the original dataset (principal component analysis), metric learning (Mahalanobis metric for clustering), and k-nearest neighbors learning algorithm (KNN), into an enhanced model, with mean performance equal to 85% by terms of the Pearson correlation coefficient (PCC) between observed and predicted values.

Keywords: aflatoxins, Aspergillus spp., dried figs, k-nearest neighbors, machine learning, prediction

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Authors: Tamunotonye Mayowa Braide, Isaac Oluwatayo

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This study investigates multidimensional poverty, and its correlates among rural households in Sekhukhune and Capricorn District municipalities (SDM & CDM) in Limpopo Province, South Africa. Primary data were collected from 407 rural households selected through purposive and simple random sampling techniques. Analytical techniques employed include descriptive statistics, principal component analysis (PCA), and the Alkire Foster (A-F) methodology. The results of the descriptive statistics showed there are more females (66%) than males (34%) in rural areas of Limpopo Province, with about 45% of them having secondary school education as the highest educational level attained and only about 3% do not have formal education. In the analysis of deprivation, eight dimensions of deprivation, constructed from 21 variables, were identified using the PCA. These dimensions include type and condition of dwelling water and sanitation, educational attainment and income, type of fuel for cooking and heating, access to clothing and cell phone, assets and fuel for light, health condition, crowding, and child health. In identifying the poor with poverty cut-off (0.13) of all indicators, about 75.9% of the rural households are deprived in 25% of the total dimensions, with the adjusted headcount ratio (M0) being 0.19. Multidimensional poverty estimates showed higher estimates of poor rural households with 71%, compared to 29%, which fall below the income poverty line. The study conducted poverty decomposition, using sub-groups within the area by examining regions and household characteristics. In SDM, there are more multidimensionally poor households than in CDM. The water and sanitation dimension is the largest contributor to the multidimensional poverty index (MPI) in rural areas of Limpopo Province. The findings can, therefore, assist in better design of welfare policy and target poverty alleviation programs and as well help in efficient resource allocation at the provincial and local municipality levels.

Keywords: Alkire-Foster methodology, Limpopo province, multidimensional poverty, principal component analysis, South Africa

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Authors: Mamoon Masud, Suleman Mazhar

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Indus river basin’s irrigation system in Pakistan is extremely complex, spanning over 50,000 km. Maintenance and monitoring of this demands enormous resources. This paper describes the development of a streamlined and low-cost autonomous underwater vehicle (AUV) for the monitoring of irrigation canals including water quality monitoring and water theft detection. The vehicle is a hovering-type AUV, designed mainly for monitoring irrigation canals, with fully documented design and open source code. It has a length of 17 inches, and a radius of 3.5 inches with a depth rating of 5m. Multiple sensors are present onboard the AUV for monitoring water quality parameters including pH, turbidity, total dissolved solids (TDS) and dissolved oxygen. A 9-DOF Inertial Measurement Unit (IMU), GY-85, is used, which incorporates an Accelerometer (ADXL345), a Gyroscope (ITG-3200) and a Magnetometer (HMC5883L). The readings from these sensors are fused together using directional cosine matrix (DCM) algorithm, providing the AUV with the heading angle, while a pressure sensor gives the depth of the AUV. 2 sonar-based range sensors are used for obstacle detection, enabling the vehicle to align itself with the irrigation canals edges. 4 thrusters control the vehicle’s surge, heading and heave, providing 3 DOF. The thrusters are controlled using a proportional-integral-derivative (PID) feedback control system, with heading angle and depth being the controller’s input and the thruster motor speed as the output. A flow sensor has been incorporated to monitor canal water level to detect water-theft event in the irrigation system. In addition to water theft detection, the vehicle also provides information on water quality, providing us with the ability to identify the source(s) of water contamination. Detection of such events can provide useful policy inputs for improving irrigation efficiency and reducing water contamination. The AUV being low cost, small sized and suitable for autonomous maneuvering, water level and quality monitoring in the irrigation canals, can be used for irrigation network monitoring at a large scale.

Keywords: the autonomous underwater vehicle, irrigation canal monitoring, water quality monitoring, underwater line tracking

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