Search results for: 3D magnetic potential vector and electric scalar potential (A

Authors: Raghad Alhusari, Farag Omar, Moustafa Fadel

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A traditional greenhouse is a metal frame agricultural building used for cultivation plants in a controlled environment isolated from external climatic changes. Using greenhouses in agriculture is an efficient way to reduce the water consumption, where agriculture field is considered the biggest water consumer world widely. Controlling greenhouse environment yields better productivity of plants but demands an increase of electric power. Although various control approaches have been used towards greenhouse automation, most of them are applied to traditional greenhouses with ventilation fans and/or evaporation cooling system. Such approaches are still demanding high energy and water consumption. The aim of this research is to develop a fuzzy control system that minimizes water and energy consumption by utilizing outside weather conditions and underground heat exchanger to maintain the optimum climate of the greenhouse. The proposed control system is implemented on an experimental model of thermally isolated greenhouse structure with dimensions of 6x5x2.8 meters. It uses fans for extracting heat from the ground heat exchanger system, motors for automatic open/close of the greenhouse windows and LED as lighting system. The controller is integrated also with environmental condition sensors. It was found that using the air-to-air horizontal ground heat exchanger with 90 mm diameter and 2 mm thickness placed 2.5 m below the ground surface results in decreasing the greenhouse temperature of 3.28 ˚C which saves around 3 kW of consumed energy. It also eliminated the water consumption needed in evaporation cooling systems which are traditionally used for cooling the greenhouse environment.

Keywords: automation, earth-to-air heat exchangers, fuzzy control, greenhouse, sustainable buildings

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Authors: Farida Azzouz-Olden, Arthur G. Hunt, Gloria Degrandi-Hoffman

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Bees are confronting several environmental challenges, including the intermingled effects of malnutrition and disease. Intuitively, pollen is the healthiest nutritional choice; however, commercial substitutes, such as BeePro and MegaBee, are widely used. Herein we examined how feeding natural and artificial diets shapes transcription in the abdomen of the honey bee, and how transcription shifts in combination with Nosema parasitism. Gene ontology enrichment revealed that, compared with poor diet (carbohydrates (C)), bees fed pollen (P > C), BeePro (B > C), and MegaBee (M > C) showed a broad upregulation of metabolic processes, especially lipids; however, pollen feeding promoted more functions and superior proteolysis. The superiority of the pollen diet was also evident through the remarkable overexpression of vitellogenin in bees fed pollen instead of MegaBee or BeePro. Upregulation of bioprocesses under carbohydrates feeding compared to pollen (C > P) provided a clear poor nutritional status, uncovering stark expression changes that were slight or absent relatively to BeePro (C > B) or MegaBee (C > M). Poor diet feeding (C > P) induced starvation response genes and hippo signaling pathway, while it repressed growth through different mechanisms. Carbohydrate feeding (C > P) also elicited ‘adult behavior’, and developmental processes suggesting transition to foraging. Finally, it altered the ‘circadian rhythm’, reflecting the role of this mechanism in the adaptation to nutritional stress in mammals. Nosema-infected bees fed pollen compared to carbohydrates (PN > CN) upheld certain bioprocesses of uninfected bees (P > C). Poor nutritional status was more apparent against pollen (CN > PN) than BeePro (CN > BN) or MegaBee (CN > MN). Nosema accentuated the effects of malnutrition since more starvation-response genes and stress response mechanisms were upregulated in CN > PN compared to C > P. The bioprocess ‘Macromolecular complex assembly’ was also enriched in CN > PN, and involved genes associated with human HIV and/or influenza, thus providing potential candidates for bee-Nosema interactions. Finally, the enzyme Duox emerged as essential for guts defense in bees, similarly to Drosophila. These results provide evidence of the superior nutritional status of bees fed pollen instead of artificial substitutes in terms of overall health, even in the presence of a pathogen.

Keywords: honeybee, immunity, Nosema, nutrition, RNA-seq

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Authors: Xiaoyu Shen, Fang Fang, Chujun Qiu

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Herewith we present a Fourier method for credit risk quantification and allocation in the factor-copula model framework. The key insight is that, compared to directly computing the cumulative distribution function of the portfolio loss via Monte Carlo simulation, it is, in fact, more efficient to calculate the transformation of the distribution function in the Fourier domain instead and inverting back to the real domain can be done in just one step and semi-analytically, thanks to the popular COS method (with some adjustments). We also show that the Euler risk allocation problem can be solved in the same way since it can be transformed into the problem of evaluating a conditional cumulative distribution function. Once the conditional or unconditional cumulative distribution function is known, one can easily calculate various risk metrics. The proposed method not only fills the niche in literature, to the best of our knowledge, of accurate numerical methods for risk allocation but may also serve as a much faster alternative to the Monte Carlo simulation method for risk quantification in general. It can cope with various factor-copula model choices, which we demonstrate via examples of a two-factor Gaussian copula and a two-factor Gaussian-t hybrid copula. The fast error convergence is proved mathematically and then verified by numerical experiments, in which Value-at-Risk, Expected Shortfall, and conditional Expected Shortfall are taken as examples of commonly used risk metrics. The calculation speed and accuracy are tested to be significantly superior to the MC simulation for real-sized portfolios. The computational complexity is, by design, primarily driven by the number of factors instead of the number of obligors, as in the case of Monte Carlo simulation. The limitation of this method lies in the "curse of dimension" that is intrinsic to multi-dimensional numerical integration, which, however, can be relaxed with the help of dimension reduction techniques and/or parallel computing, as we will demonstrate in a separate paper. The potential application of this method has a wide range: from credit derivatives pricing to economic capital calculation of the banking book, default risk charge and incremental risk charge computation of the trading book, and even to other risk types than credit risk.

Keywords: credit portfolio, risk allocation, factor copula model, the COS method, Fourier method

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Authors: Zachary Huffman, Joana Rocha

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Wall-pressure fluctuations induced by the turbulent boundary layer (TBL) developed over aircraft are a significant source of aircraft cabin noise. Since the power spectral density (PSD) of these pressure fluctuations is directly correlated with the amount of sound radiated into the cabin, the development of accurate empirical models that predict the PSD has been an important ongoing research topic. The sound emitted can be represented from the pressure fluctuations term in the Reynoldsaveraged Navier-Stokes equations (RANS). Therefore, early TBL empirical models (including those from Lowson, Robertson, Chase, and Howe) were primarily derived by simplifying and solving the RANS for pressure fluctuation and adding appropriate scales. Most subsequent models (including Goody, Efimtsov, Laganelli, Smol’yakov, and Rackl and Weston models) were derived by making modifications to these early models or by physical principles. Overall, these models have had varying levels of accuracy, but, in general, they are most accurate under the specific Reynolds and Mach numbers they were developed for, while being less accurate under other flow conditions. Despite this, recent research into the possibility of using alternative methods for deriving the models has been rather limited. More recent studies have demonstrated that an artificial neural network model was more accurate than traditional models and could be applied more generally, but the accuracy of other machine learning techniques has not been explored. In the current study, an original model is derived using a stepwise regression algorithm in the statistical programming language R, and TBL wall-pressure fluctuations PSD data gathered at the Carleton University wind tunnel. The theoretical advantage of a stepwise regression approach is that it will automatically filter out redundant or uncorrelated input variables (through the process of feature selection), and it is computationally faster than machine learning. The main disadvantage is the potential risk of overfitting. The accuracy of the developed model is assessed by comparing it to independently sourced datasets.

Keywords: aircraft noise, machine learning, power spectral density models, regression models, turbulent boundary layer wall-pressure fluctuations

Procedia PDF Downloads 135

Authors: Emma O’ Keeffe, Helen Hughes, Peter McLoughlin, Shiau Pin Tan, Nick McCarthy

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Biosecurity is emerging as one of the most important issues facing the agricultural and forestry community. This is as a result of increased invasion from new pests and diseases with the main protocol for dealing with these species being the use of synthetic pesticides. However, these chemicals have been shown to exhibit negative effects on the environment. Seaweeds represent a vast untapped resource of bio-molecules with a broad range of biological activities including pesticidal. This project investigated both the antifungal and antibacterial activity of seaweed species against two problematic root rot fungi, Armillaria mellea and Heterobasidion annosum and ten quarantine bacterial plant pathogens including Xanthomonas arboricola, Xanthomonas fragariae, and Erwinia amylovora. Four seaweed species were harvested from the South-East coast of Ireland including brown, red and green varieties. The powdered seaweeds were extracted using four different solvents by liquid extraction. The poisoned food technique was employed to establish the antifungal efficacy, and the standard disc diffusion assay was used to assess the antibacterial properties of the seaweed extracts. It was found that extracts of the green seaweed exhibited antifungal activity against H. annosum, with approximately 50% inhibition compared to the negative control. The protectant activities of the active extracts were evaluated on disks of Picea sitchensis, a plant species sensitive to infection from H. annosum and compared to the standard chemical control product urea. The crude extracts exhibited very similar activity to the 10% and 20% w/v concentrations of urea, demonstrating the ability of seaweed extracts to compete with commercially available products. Antibacterial activity was exhibited by a number of seaweed extracts with the red seaweed illustrating the strongest activity, with a zone of inhibition of 15.83 ± 0.41 mm exhibited against X. arboricola whilst the positive control (10 μg/disk of chloramphenicol) had a zone of 26.5 ± 0.71 mm. These results highlight the potential application of seaweed extracts in the forestry and agricultural industries for use as biopesticides. Further work is now required to identify the bioactive molecules that are responsible for this antifungal and antibacterial activity in the seaweed extracts, including toxicity studies to ensure the extracts are non-toxic to plants and humans.

Keywords: antibacterial, antifungal, biopesticides, seaweeds

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Authors: Atitallah Sofien, Bouyahia Olfa, Attar Souleima, Missaoui Nada, Ben Rabeh Rania, Yahyaoui Salem, Mazigh Sonia, Boukthir Samir

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Introduction: Ciliopathies are a spectrum of diseases that have in common a defect in the synthesis of ciliary proteins. It is a rare cause of neonatal cholestasis. Clinical presentation varies extremely, and the main affected organs are the kidneys, liver, and pancreas. Methodology: This is a descriptive case report of a newborn who was admitted for exploration of neonatal cholestasis in the Paediatric Department C at the Children’s Hospital of Tunis, where the investigations concluded with a rare genetic mutation. Results: This is the case of a newborn male with no family history of hepatic and renal diseases, born to consanguineous parents, and from a well-monitored uneventful pregnancy. He developed jaundice on the second day of life, for which he received conventional phototherapy in the neonatal intensive care unit. He was admitted at 15 days for mild bronchiolitis. On clinical examination, intense jaundice was noted with normal stool and urine colour. Initial blood work showed an elevation in conjugated bilirubin and a high gamma-glutamyl transferase level. Transaminases and prothrombin time were normal. Abdominal sonography revealed hepatomegaly, splenomegaly, and undifferentiated renal cortex with bilateral medullar micro-cysts. Kidney function tests were normal. The infant received ursodeoxycholic acid and vitamin therapy. Genetic testing showed a homozygous mutation in the DCDC2 gene that hadn’t been documented before confirming the diagnosis of renal-hepatic ciliopathy. The patient has regular follow-ups, and his conjugated bilirubin and gamma-glutamyl transferase levels have been decreasing. Conclusion: Genetic testing has revolutionized the approach to etiological diagnosis in pediatric cholestasis. It enables personalised treatment strategies to better enhance the quality of life of patients and prevent potential complications following adequate long-term monitoring.

Keywords: cholestasis, newborn, ciliopathy, DCDC2, genetic

Procedia PDF Downloads 63

Authors: Vinay Singh, Chandra Deo, Asit Chakrabarti, Lopamudra Sahoo, Mahak Singh, Rakesh Kumar, Dinesh Kumar, H. Bharati, Biswajit Das, V. K. Mishra

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The most widely used feed resources in poultry feed, like maize and soybean, are expensive as well as in short supply. Hence, there is a need to utilize non-conventional feed ingredients to cut down feed costs. As an alternative, brewery by-products like brewers’ dried grains are potential non-conventional feed resources. North-East India is inhabited by many tribes, and most of these tribes prepare their indigenous local brew, mostly using rice grains as the primary substrate. Choak, a homemade rice beer waste, is an excellent and cheap source of protein and other nutrients. Fresh homemade rice beer waste (rice brewer’s grain) was collected locally. The proximate analysis indicated 28.53% crude protein, 92.76% dry matter, 5.02% ether extract, 7.83% crude fibre, 2.85% total ash, 0.67% acid insoluble ash, 0.91% calcium, and 0.55% total phosphorus. A feeding trial with 5 treatments (incorporating rice beer waste at the inclusion levels of 0,10,20,30 & 40% by replacing maize and soybean from basal diet) was conducted with 25 laying hens per treatment for 16 weeks under completely randomized design in order to study the production performance, blood-biochemical parameters, immunity, egg quality and cost economics of laying hens. The results showed substantial variations (P<0.01) in egg production, egg mass, FCR per dozen eggs, FCR per kg egg mass, and net FCR. However, there was not a substantial difference in either body weight or feed intake or in egg weight. Total serum cholesterol reduced significantly (P<0.01) at 40% inclusion of rice beer waste. Additionally, the egg haugh unit grew considerably (P<0.01) when the graded levels of rice beer waste increased. The inclusion of 20% rice brewers dried grain reduced feed cost per kg egg mass and per dozen egg production by Rs. 15.97 and 9.99, respectively. Choak (homemade rice beer waste) can thus be safely incorporated into the diet of laying hens at a 20% inclusion level for better production performance and cost-effectiveness.

Keywords: choak, rice beer waste, laying hen, production performance, cost economics

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Authors: G. Singh, H.Schuster, U. Füssel

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The stability of electric arc and durability of electrode tip used in Tungsten Inert Gas (TIG) welding demand a metallurgical study about the chemical transport mechanism of emitter oxide particles in tungsten electrode during its real welding conditions. The tungsten electrodes doped with emitter oxides of rare earth oxides such as La₂O₃, Th₂O₃, Y₂O₃, CeO₂ and ZrO₂ feature a comparatively lower work function than tungsten and thus have superior emission characteristics due to lesser surface temperature of the cathode. The local change in concentration of these emitter particles in tungsten electrode due to high temperature diffusion (chemical transport) can change its functional properties like electrode temperature, work function, electron emission, and stability of the electrode tip shape. The resulting increment in tip surface temperature results in the electrode material loss. It was also observed that the tungsten recrystallizes to large grains at high temperature. When the shape of grain boundaries are granular in shape, the intergranular diffusion of oxide emitter particles takes more time to reach the electrode surface. In the experimental work, the microstructure of the used electrode's tip surface will be studied by scanning electron microscope and reflective X-ray technique in order to gauge the extent of the diffusion and chemical reaction of emitter particles. Besides, a simulated model is proposed to explain the effect of oxide particles diffusion on the electrode’s microstructure, electron emission characteristics, and electrode tip erosion. This model suggests metallurgical modifications in tungsten electrode to enhance its erosion resistance.

Keywords: rare-earth emitter particles, temperature-dependent diffusion, TIG welding, Tungsten electrode

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Authors: Iman Farasat, Howard M. Salis

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The CRISPR/Cas9 system has revolutionized genome engineering by enabling site-directed and high-throughput genome editing, genome insertion, and gene knockdowns in several species, including bacteria, yeast, flies, worms, and human cell lines. This technology has the potential to enable human gene therapy to treat genetic diseases and cancer at the molecular level; however, the current CRISPR/Cas9 system suffers from seemingly sporadic off-target genome mutagenesis that prevents its use in gene therapy. A comprehensive mechanistic model that explains how the CRISPR/Cas9 functions would enable the rational design of the guide-RNAs responsible for target site selection while minimizing unexpected genome mutagenesis. Here, we present the first quantitative model of the CRISPR/Cas9 genome mutagenesis system that predicts how guide-RNA sequences (crRNAs) control target site selection and cleavage activity. We used statistical thermodynamics and law of mass action to develop a five-step biophysical model of cas9 cleavage, and examined it in vivo and in vitro. To predict a crRNA's binding specificities and cleavage rates, we then compiled a nearest neighbor (NN) energy model that accounts for all possible base pairings and mismatches between the crRNA and the possible genomic DNA sites. These calculations correctly predicted crRNA specificity across 5518 sites. Our analysis reveals that cas9 activity and specificity are anti-correlated, and, the trade-off between them is the determining factor in performing an RNA-mediated cleavage with minimal off-targets. To find an optimal solution, we first created a scheme of safe-design criteria for Cas9 target selection by systematic analysis of available high throughput measurements. We then used our biophysical model to determine the optimal Cas9 expression levels and timing that maximizes on-target cleavage and minimizes off-target activity. We successfully applied this approach in bacterial and mammalian cell lines to reduce off-target activity to near background mutagenesis level while maintaining high on-target cleavage rate.

Keywords: biophysical model, CRISPR, Cas9, genome editing

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Authors: Giulia Balestracci

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Pakistan is endowed with diversified natural capital spanning along the 1000-kilometer-long coastline, shared by the coastal provinces of Sindh and Balochistan. It includes some of the most diverse, extensive, and least disturbed reef areas in the Indian Ocean. Pakistani marine and coastal ecosystems are fundamental for the social and economic well-being of the region. They support economic activities such as fishing, shrimp farming, tourism, and shipping, which contribute to income, food security, and the livelihood of millions of people. The coastal regions of Sindh and Balochistan are rich in natural resources and diverse ecosystems, and host also rural coastal communities that have been the keepers of rich cultural legacies and pristine natural landscapes. However, significant barriers hinder tourism development, such as the daunting socio-economic challenges, including the post-COVID-19 scenario, forced migration, institutional gaps, and the ravages of climate change. Pakistan holds immense potential for the tourism sector development within the framework of a sustainable blue economy, thereby fostering greener economic growth and employment opportunities, securing financing for the protection and conservation of its coastal and marine natural assets. Based on the assessment of Pakistan’s natural and cultural coastal and maritime tourism resources, a deep study of the regulatory and institutional aspects of the tourism sector in the country accompanied by the SWOT analysis and accompanied by an in-depth interview with a member of the Pakistan National Tourism Coordination Board (NTCB). A market analysis has been developed, and Lao PDR, Thailand, and Indonesia’s ecotourism development have been analyzed under a comparative analysis length to recommend some nature-based tourism activities for the sustainable development of the coastal areas in Pakistan. Nature-based tourism represents a win-win option as it uses economic incentives for the protection and cultural uses of natural resources. This article stresses the importance of nature-based activities for blue tourism, aligning conservation with developmental goals to safeguard natural resources and cultural heritage, all while fostering economic prosperity.

Keywords: blue tourism, coastal Pakistan, nature-based tourism, sustainable blue economy, sustainable development

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Authors: Nilanthi Ratnayake, Wen-Ling Liu

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Prior research indicates an increasing gap between the skills and capabilities of graduates in the contemporary workplace across the globe. The challenge of addressing this issue primarily lies on the hands of higher education institutes/universities. In particular, surveys of UK employers and retailers found that soft skills including communication, numeracy, teamwork, confidence, analytical ability, digital/IT skills, business sense, language, and social skills are highly valued by graduate employers, and in achieving this, there are various assessed and non-assessed learning exercises have already been embedded into the university curriculum. To this end, this research study aims to explore the reflections of postgraduate student participation in a live commercial project (i.e. designing an advertising campaign for open days, summer school etc.) implemented with the intention of offering a transformative experience by deploying this project. Qualitative research methodology has been followed in this study, collecting data from three types of target audiences; students, academics and employers via a series of personal interviews and focus group discussions. Recorded data were transcribed, entered into NVIVO, and analysed using meaning condensation and content analysis. Students reported that they had a very positive impact towards improving self-efficacy, especially in relation to soft skills and confidence in seeking employment opportunities. In addition, this project has reduced cultural barriers for international students in general communications. Academic staff and potential employers who attended on the presentation day expressed their gratitude for offering a lifelong experience for students, and indeed believed that these type of projects contribute significantly to enhance skills and capabilities of students to cater the demands of employers. In essence, key findings demonstrate that an integration of knowledge-based skills into a live commercial project facilitate individuals to make the transition from education to employment in terms of skills, abilities and work behaviours more effectively in comparison to some other activities/assuagements that are currently in place in higher education institutions/universities.

Keywords: soft skills, commercially live project, higher education, student participation

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Authors: Patience Muchini, Electdom Matandiroya, Emmanuel Mashonjowa

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A ground-end manifestation of space weather phenomena is known as geomagnetically induced currents (GICs). GICs flow along the electric power transmission cables connecting the transformers and between the grounding points of power transformers during significant geomagnetic storms. Geomagnetically induced currents have been studied in other regions and have been noted to affect the power grid network. In Zimbabwe, grid failures have been experienced, but it is yet to be proven if these failures have been due to GICs. The purpose of this paper is to characterize geomagnetically induced currents with a power grid network. This paper analyses data collected, which is geomagnetic data, which includes the Kp index, DST index, and the G-Scale from geomagnetic storms and also analyses power grid data, which includes reactive power, relay tripping, and alarms from high voltage substations and then correlates the data. This research analysis was first theoretically analyzed by studying geomagnetic parameters and then experimented upon. To correlate, MATLAB was used as the basic software to analyze the data. Latitudes of the substations were also brought into scrutiny to note if they were an impact due to the location as low latitudes areas like most parts of Zimbabwe, there are less severe geomagnetic variations. Based on theoretical and graphical analysis, it has been proven that there is a slight relationship between power system failures and GICs. Further analyses can be done by implementing measuring instruments to measure any currents in the grounding of high-voltage transformers when geomagnetic storms occur. Mitigation measures can then be developed to minimize the susceptibility of the power network to GICs.

Keywords: adverse space weather, DST index, geomagnetically induced currents, KP index, reactive power

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Authors: Tekle Olbamo, Tegegn Tesfaye, Dikaso Unbushe, Belete Jorga

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Bovine trypanosomosis is a haemoprotozoan parasitic disease, mostly transmitted by the tsetse fly (Glossina species) and poses significant losses to the livestock industry in pastoral and agro-pastoral areas. Therefore, the current study was aimed to determine the prevalence of bovine trypanosomosis and its vectorial density in some selected tsetse suppression and non-tsetse suppression areas of South Omo Zonefrom December 2018- November 2019. Dark phase contrast buffy coat, hematocrit techniques, and thin blood smear method were used for determination of prevalence and packed cell volume of trypanosomosis infection, respectively. For entomological investigation, 96 NGU traps were deployed (64 traps in tsetse suppression areas, 32 traps in tsetse non-suppression areas) in vector breeding areas. The overall prevalence of bovine trypanosomosis was 11.05% (142/1284), and overall seasonal prevalence of disease was 14.33% (92/642) and 7.78% (50/642) for dry and wet seasons, respectively. There was a statistically significant difference (P <0.05) in disease prevalence between the two seasons. Trypanosomacongolensewas the dominant parasite species; 80% and 71.64%, followed by Trypanosomavivax. Overall mean packed cell volume indicated parasitaemic animals (23.57±3.13) had significantly lower PCV than aparasitaemic animals (27.80±4.95), and animals examined during dry season (26.22±4.37) had lower mean PCV than animals examined during wet season with the significant association. Entomological study result revealed a total of 2.64 F/T/D and 2.03 F/T/D respectively from tsetse suppression areas and tsetse non-suppression areas during dry season and 0.42 F/T/D and 0.56 F/T/D during the wet season. Glossinapallidipes was the only cyclical vectors collected and identified from current study areas along with numerous mechanical vectors of genus Tabanus, Stomoxys, and Haematopota. Therefore integrated and safe control and prevention effort should be engaged to uphold cattle production and productivity in the area.

Keywords: bovine trypanosomiasis, South Omo, tsetse fly density, epidemiological study

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Authors: Pamela-Zoe Topalli, Niina Junttila, Päivi M. Niemi, Klaus Ranta

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Adolescents’ biased perceptions about their social competence (SC), whether negatively or positively, serve to influence their socioemotional adjustment such as early feelings of social phobia (nowadays referred to as Social Anxiety Disorder-SAD). Despite the importance of biased self-perceptions in adolescents’ psychosocial adjustment, the extent to which discrepancies between self- and others’ evaluations of one’s SC are linked to social phobic symptoms remains unclear in the literature. This study examined the perceptual discrepancy profiles between self- and peers’ as well as between self- and teachers’ evaluations of adolescents’ SC and the interrelations of these profiles with self-reported social phobic symptoms. The participants were 390 3^rd graders (15 years old) of Finnish lower secondary school (50.8% boys, 49.2% girls). In contrast with variable-centered approaches that have mainly been used by previous studies when focusing on this subject, this study used latent profile analysis (LPA), a person-centered approach which can provide information regarding risk profiles by capturing the heterogeneity within a population and classifying individuals into groups. LPA revealed the following five classes of discrepancy profiles: i) extremely negatively biased perceptions of SC, ii) negatively biased perceptions of SC, iii) quite realistic perceptions of SC, iv) positively biased perceptions of SC, and v) extremely positively biased perceptions of SC. Adolescents with extremely negatively biased perceptions and negatively biased perceptions of their own SC reported the highest number of social phobic symptoms. Adolescents with quite realistic, positively biased and extremely positively biased perceptions reported the lowest number of socio-phobic symptoms. The results point out the negatively and the extremely negatively biased perceptions as possible contributors to social phobic symptoms. Moreover, the association of quite realistic perceptions with low number of social phobic symptoms indicates its potential protective power against social phobia. Finally, positively and extremely positively biased perceptions of SC are negatively associated with social phobic symptoms in this study. However, the profile of extremely positively biased perceptions might be linked as well with the existence of externalizing problems such as antisocial behavior (e.g. disruptive impulsivity). The current findings highlight the importance of considering discrepancies between self- and others’ perceptions of one’s SC in clinical and research efforts. Interventions designed to prevent or moderate social phobic symptoms need to take into account individual needs rather than aiming for uniform treatment. Implications and future directions are discussed.

Keywords: adolescence, latent profile analysis, perceptual discrepancies, social competence, social phobia

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Authors: Pradeepa Teegala, Ramreddy Chitteti

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This article analyzes the mixed convection flow of chemically reacting micropolar fluid over a truncated cone embedded in non-Darcy porous medium with convective boundary condition. In addition, heat generation/absorption and Joule heating effects are taken into consideration. The similarity solution does not exist for this complex fluid flow problem, and hence non-similarity transformations are used to convert the governing fluid flow equations along with related boundary conditions into a set of nondimensional partial differential equations. Many authors have been applied the spectral quasi-linearization method to solve the ordinary differential equations, but here the resulting nonlinear partial differential equations are solved for non-similarity solution by using a recently developed method called the spectral quasi-linearization method (SQLM). Comparison with previously published work on special cases of the problem is performed and found to be in excellent agreement. The effect of pertinent parameters namely, Biot number, mixed convection parameter, heat generation/absorption, Joule heating, Forchheimer number, chemical reaction, micropolar and magnetic field on physical quantities of the flow are displayed through graphs and the salient features are explored in detail. Further, the results are analyzed by comparing with two special cases, namely, vertical plate and full cone wherever possible.

Keywords: chemical reaction, convective boundary condition, joule heating, micropolar fluid, mixed convection, spectral quasi-linearization method

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Authors: Sruthi Raveendran

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Developmental dyslexia, characterized by reading difficulties despite normal intelligence, presents a significant challenge for bilingual children navigating languages with varying degrees of orthographic transparency. This study bridges a critical gap in dyslexia interventions for bilingual populations in India by examining how consistency and predictability of letter-sound relationships in a writing system (orthographic transparency) influence the ability to understand and manipulate the building blocks of sound in language (phonological processing). The study employed a computerized visual rhyme-judgment task with concurrent EEG (electroencephalogram) recording. The task compared reaction times, accuracy of performance, and event-related potential (ERP) components (N170, N400, and LPC) for rhyming and non-rhyming stimuli in two orthographies: English (opaque orthography) and Kannada (transparent orthography). As hypothesized, the results revealed advantages in phonological processing tasks for transparent orthography (Kannada). Children with dyslexia were faster and more accurate when judging rhymes in Kannada compared to English. This suggests that a language with consistent letter-sound relationships (transparent orthography) facilitates processing, especially for tasks that involve manipulating sounds within words (rhyming). Furthermore, brain activity measured by event-related potentials (ERP) showed less effort required for processing words in Kannada, as reflected by smaller N170, N400, and LPC amplitudes. These findings highlight the crucial role of orthographic transparency in optimizing reading performance for bilingual children with dyslexia. These findings emphasize the need for language-specific intervention strategies that consider the unique linguistic characteristics of each language. While acknowledging the complexity of factors influencing dyslexia, this research contributes valuable insights into the impact of orthographic transparency on phonological awareness in bilingual children. This knowledge paves the way for developing tailored interventions that promote linguistic inclusivity and optimize literacy outcomes for children with dyslexia.

Keywords: developmental dyslexia, phonological awareness, rhyme judgment, orthographic transparency, Kannada, English, N170, N400, LPC

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Authors: Madelaine Venzon, Dany S. S. L. Amaral, André L. Perez, Natália S. Diaz, Juliana A. Martinez Chiguachi, Maira C. M. Fonseca, James D. Harwood, Angelo Pallini

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Integrating strategies of sustainable crop production and promoting the provisioning of ecological services on farms and within rural landscapes is a challenge for today’s agriculture. Habitat management, through increasing vegetational diversity, enhances heterogeneity in agroecosystems and has the potential to improve the recruitment of natural enemies of pests, which promotes biological control services. In tropical agroecosystems, however, there is a paucity of information pertaining to the resources provided by associated plants and their interactions with natural enemies. The maintenance of non-crop plants integrated into and/or surrounding crop fields provides the farmer with a low-investment option to enhance biological control. We carried out field experiments in chili pepper agroecosystems with small stakeholders located in the Zona da Mata, State of Minas Gerais, Brazil, from 2011 to 2015 where we assessed: (a) whether non-crop plants within and around chili pepper fields affect the diversity and abundance of aphidophagous species; (b) whether there are direct interactions between non-crop plants and aphidophagous arthropods; and (c) the importance of non-crop plant resources for survival of Coccinellidae and Chrysopidae species. Aphidophagous arthropods were dominated by Coccinellidae, Neuroptera, Syrphidae, Anthocoridae and Araneae. These natural enemies were readily observed preying on aphids, feeding on flowers or extrafloral nectaries and using plant structures for oviposition and/or protection. Aphid populations were lower on chili pepper fields associated with non-crop plants that on chili pepper monocultures. Survival of larvae and adults of different species of Coccinellidae and Chrysopidae on non-crop resources varied according to the plant species. This research provides evidence that non-crop plants in chili pepper agroecosystems can affect aphid abundance and their natural enemy abundance and survival. It is also highlighting the need for further research to fully characterize the structure and function of plant resources in these and other tropical agroecosystems. Financial support: CNPq, FAPEMIG and CAPES (Brazil).

Keywords: Conservation biological control, aphididae, Coccinellidae, Chrysopidae, plant diversification

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Authors: Mrinal Gupta, Mohammad Rumman, Babita Singh Abbas Ali Mahdi, Shivani Pandey

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Introduction: Berberine (BBR), a bioactive compound isolated from Coptidis Rhizoma, possesses diverse pharmacological activities, including anti-bacterial, anti-inflammatory, antitumor, hypolipidemic, and anti-diabetic. However, its role as an anti-diabetic agent in animal models of dexamethasone (Dex)-induced diabetes remains unknown. Studies have shown that natural compounds, including aloe, caper, cinnamon, cocoa, green and black tea, and turmeric, can be used for treating Type 2 diabetes mellitus (DM). Compared to conventional drugs, natural compounds have fewer side effects and are easily available. Herein, we studied the anti-diabetic effects of BBR in a mice model of Dex-induced diabetes. Methods: HepG2 cell line was used for glucose release and glycogen synthesis studies. Cell proliferation was measured by methylthiotetrazole (MTT) assay. For animal studies, mice were treated with Dex (2 mg/kg, i.m.) for 30 days and the effect of BBR at the doses 100, 200, and 500 mg/kg (p.o.) was analyzed. Glucose, insulin, and pyruvate tests were performed to evaluate the development of the diabetic model. An echo MRI was performed to assess the fat mass. Further, to elucidate the mechanism of action of BBR, mRNA expression of genes regulating gluconeogenesis, glucose uptake, and glycolysis were analyzed. Results: In vitro BBR had no impact on cell viability up to a concentration of 50μM. Moreover, BBR suppressed the hepatic glucose release and improved glucose tolerance in HepG2 cells. In vivo, BBR improved glucose homeostasis in diabetic mice, as evidenced by enhanced glucose clearance, increased glycolysis, elevated glucose uptake, and decreased gluconeogenesis. Further, Dex treatment increased the total fat mass in mice, which was ameliorated by BBR treatment. Conclusion: BBR improves glucose tolerance by increasing glucose clearance, inhibiting hepatic glucose release, and decreasing obesity. Thus, BBR may become a potential therapeutic agent for treating glucocorticoid-induced diabetes and obesity in the future.

Keywords: glucocorticoid, hyperglycemia, berberine, HepG2 cells, insulin resistance, glucose

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Authors: Shafiqul Islam, Cordia Chu

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Bangladesh is one of the most vulnerable countries to climate related disasters such as flood and cyclone. Exploring from the semi-structured in-depth interviews of 38 stakeholders and literature review, this study examined the public spending distribution process in DRR. This paper demonstrates how the processes of political economy-enclosure, exclusion, encroachment, and entrenchment hinder the Disaster Risk Reduction (DRR) efforts of Department of Disaster Management (DDM) such as distribution of flood centres, cyclone centres and 40 days employment generation programs. Enclosure refers to when DRR projects allocated to less vulnerable areas or expand the roles of influencing actors into the public sphere. Exclusion refers to when DRR projects limit affected people’s access to resources or marginalize particular stakeholders in decision-making activities. Encroachment refers to when allocation of DRR projects and selection of location and issues degrade the environmental affect or contribute to other forms of disaster risk. Entrenchment refers to when DRR projects aggravate the disempowerment of common people worsen the concentrations of wealth and income inequality within a community. In line with United Nations (UN) Sustainable Development Goals (SDGs), Hyogo and Sendai Frameworks, in the case of Bangladesh, DRR policies implemented under the country’s national five-year plan, disaster-related acts and rules. These policies and practices have somehow enabled influential-elites to mobilize and distribute resources through bureaucracies. Exclusionary forms of fund distribution of DRR exist at both the national and local scales. DRR related allocations have encroached through the low land areas development project without consulting local needs. Most severely, DRR related unequal allocations have entrenched social class trapping the backward communities vulnerable to climate related disasters. Planners and practitioners of DRR need to take necessary steps to eliminate the potential risks from the processes of enclosure, exclusion, encroachment, and entrenchment happens in project fund allocations.

Keywords: Bangladesh, disaster risk reduction, fund distribution, political economy

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Authors: Fana Woldetsadik

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Ethiopia has a huge livestock population, including sheep, that has been contributing a considerable portion to the economy of the country and still promising to rally around the economic advancement of the country. However, feed shortage is a limiting factor in the production and productivity of sheep among Ethiopian smallholder farmers. Therefore, the aim of this study was to prove the role of the locally available brewery by-products called dried Atella as a supplement in feed intake, digestibility, live weight gain, carcass yield, and economic benefit in comparison with commercially purchased supplements known as niger seed cake (NSC). This on-station feeding experiment was conducted on the Zenzelma Campus of Bahir Dar University animal farm. The experimental design used for this research was a completely randomized design (CRD) with five replications. The crude protein (CP) content of dried Atella, wheat bran (WB), natural pasture hay (NPH) and NSC were about 25.07%, 16.57%, 4.48% and 38.04%, respectively, while the neutral detergent fibre (NDF),acid detergent fibre (ADF) and acid detergent lignin (ADL) content of dried Atella, WB, NPH and NSC were around 31.75%, 8.31%, 8.14%; 42.05%, 22.64%, 4.04%; 74.21%, 50.81%, 8.66%; 42.31%, 26.95% and 6.9%, respectively. The result depicted that a higher(P < 0.001) feed intake, nutrient intake, and digestibility for lambs supplemented with Atella than those supplemented with NSC. Furthermore, daily body weight gain and carcass characteristics were better (P < 0.05) for the sheep supplemented with dried Atella than NSC. On the other hand, in terms of profitability, although there was no substantial difference (P > 0.05) between T2 (animals fed NPH,NSC and WB) and T3 (animals fed NPH, Atella and WB), slightly better benefit was recorded in T3 groups. However, loss of money was recorded in T1 (animals fed NPH and WB). Hence, from the biological performance of lambs, it was concluded that Atella could be a potential supplementary feed for sheep fattening among smallholder farmers than NSC despite no profitability difference. Nevertheless, further investigation is recommended to examine the consequence of supplementation of NPH with NSC and NPH with Atella on fatty acid profile analysis, the physicochemical composition of meat, and meat composition.

Keywords: Attela, Bahir Dar university, Carcass yield, digestibility, natural pasture hay, Niger seed cake, smallholder farmers, weight gain, Ethiopia

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Authors: Lanchi Xie, Zhihui Li, Zhigang Li, Guiqiang Wang, Lei Xu, Yuwen Yan

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Image-based facial features can be classified into category recognition features and individual recognition features. Current automated face recognition systems extract a specific feature vector of different dimensions from a facial image according to their pre-trained neural network. However, to improve the efficiency of parameter calculation, an algorithm generally reduces the image details by pooling. The operation will overlook the details concerned much by forensic experts. In our experiment, we adopted a variety of face recognition algorithms based on deep learning, compared a large number of naturally collected face images with the known data of the same person's frontal ID photos. Downscaling and manual handling were performed on the testing images. The results supported that the facial recognition algorithms based on deep learning detected structural and morphological information and rarely focused on specific markers such as stains and moles. Overall performance, distribution of genuine scores and impostor scores, and likelihood ratios were tested to evaluate the accuracy of biometric systems and forensic experts. Experiments showed that the biometric systems were skilled in distinguishing category features, and forensic experts were better at discovering the individual features of human faces. In the proposed approach, a fusion was performed at the score level. At the specified false accept rate, the framework achieved a lower false reject rate. This paper contributes to improving the interpretability of the objective method of facial comparison and provides a novel method for human-machine collaboration in this field.

Keywords: likelihood ratio, automated facial recognition, facial comparison, biometrics

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Authors: Linda Dusman, Linda Baker

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The Bearman Study of Audience Engagement examined the potential for real-time music education during online symphony orchestra concerts. It follows on the promising results of a preliminary study of STEAM (Science, Technology, Engineering, Arts, and Mathematics) education during live concerts, funded by the National Science Foundation with the Baltimore Symphony Orchestra. For the Bearman Study, audience groups were recruited to attend two previously recorded concerts of the National Orchestral Institute (NOI) in 2020 or the Utah Symphony in 2021. They used a smartphone app called EnCue to present real-time program notes about the music being performed. Short notes along with visual information (photos and score fragments) were designed to provide historical, cultural, biographical, and theoretical information at specific moments in the music where that information would be most pertinent, generally spaced 2-3 minutes apart to avoid distraction. The music performed included Dvorak Symphony No. 8 and Mahler Symphony No. 5 at NOI, and Mendelssohn Scottish Symphony and Richard Strauss Metamorphosen with the Utah Symphony, all standard repertoire for symphony orchestras. During each phase of the study (2020 and 2021), participants were randomly assigned to use the app to view program notes during the first concert or to use the app during the second concert. A total of 139 participants (67 in 2020 and 72 in 2021) completed three online questionnaires, one before attending the first concert, one immediately after the concert, and the third immediately after the second concert. Questionnaires assessed demographic background, expertise in music, engagement during the concert, learning of content about the composers and the symphonies, and interest in the future use of the app. In both phases of the study, participants demonstrated that they learned content presented on the app, evidenced by the fact that their multiple-choice test scores were significantly higher when they used the app than when they did not. In addition, most participants indicated that using the app enriched their experience of the concert. Overall, they were very positive about their experience using the app for real-time learning and they expressed interest in using it in the future at both live and streaming concerts. Results confirmed that informal real-time learning during concerts is possible and can generate enhanced engagement and interest in classical music.

Keywords: audience engagement, informal education, music technology, real-time learning

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Authors: Sunday B. Alabi, Edikan P. Felix, Aniediong M. Umo

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Crystal size distribution is of great importance in the sugar factories. It determines the market value of granulated sugar and also influences the cost of production of sugar crystals. Typically, sugar is produced using fed-batch vacuum evaporative crystallizer. The crystallization quality is examined by crystal size distribution at the end of the process which is quantified by two parameters: the average crystal size of the distribution in the mean aperture (MA) and the width of the distribution of the coefficient of variation (CV). Lack of real-time measurement of the sugar crystal size hinders its feedback control and eventual optimisation of the crystallization process. An attractive alternative is to use a soft sensor (model-based method) for online estimation of the sugar crystal size. Unfortunately, the available models for sugar crystallization process are not suitable as they do not contain variables that can be measured easily online. The main contribution of this paper is the development of a regression model for estimating the sugar crystal size as a function of input variables which are easy to measure online. This has the potential to provide real-time estimates of crystal size for its effective feedback control. Using 7 input variables namely: initial crystal size (Lo), temperature (T), vacuum pressure (P), feed flowrate (Ff), steam flowrate (Fs), initial super-saturation (S0) and crystallization time (t), preliminary studies were carried out using Minitab 14 statistical software. Based on the existing sugar crystallizer models, and the typical ranges of these 7 input variables, 128 datasets were obtained from a 2-level factorial experimental design. These datasets were used to obtain a simple but online-implementable 6-input crystal size model. It seems the initial crystal size (Lₒ) does not play a significant role. The goodness of the resulting regression model was evaluated. The coefficient of determination, R² was obtained as 0.994, and the maximum absolute relative error (MARE) was obtained as 4.6%. The high R² (~1.0) and the reasonably low MARE values are an indication that the model is able to predict sugar crystal size accurately as a function of the 6 easy-to-measure online variables. Thus, the model can be used as a soft sensor to provide real-time estimates of sugar crystal size during sugar crystallization process in a fed-batch vacuum evaporative crystallizer.

Keywords: crystal size, regression model, soft sensor, sugar, vacuum evaporative crystallizer

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Authors: Joshua Buli, David Pietrowski, Samuel Britton

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Processing SAR data usually requires constraints in extent in the Fourier domain as well as approximations and interpolations onto a planar surface to form an exploitable image. This results in a potential loss of data requires several interpolative techniques, and restricts visualization to two-dimensional plane imagery. The data can be interpolated into a ground plane projection, with or without terrain as a component, all to better view SAR data in an image domain comparable to what a human would view, to ease interpretation. An alternate but computationally heavy method to make use of more of the data is the basis of this research. Pre-processing of the SAR data is completed first (matched-filtering, motion compensation, etc.), the data is then range compressed, and lastly, the contribution from each pulse is determined for each specific point in space by searching the time history data for the reflectivity values for each pulse summed over the entire collection. This results in a per-3D-point reflectivity using the entire collection domain. New advances in GPU processing have finally allowed this rapid projection of acquired SAR data onto any desired reference surface (called backprojection). Mathematically, the computations are fast and easy to implement, despite limitations in SAR phase history data size and 3D-point cloud size. Backprojection processing algorithms are embarrassingly parallel since each 3D point in the scene has the same reflectivity calculation applied for all pulses, independent of all other 3D points and pulse data under consideration. Therefore, given the simplicity of the single backprojection calculation, the work can be spread across thousands of GPU threads allowing for accurate reflectivity representation of a scene. Furthermore, because reflectivity values are associated with individual three-dimensional points, a plane is no longer the sole permissible mapping base; a digital elevation model or even a cloud of points (collected from any sensor capable of measuring ground topography) can be used as a basis for the backprojection technique. This technique minimizes any interpolations and modifications of the raw data, maintaining maximum data integrity. This innovative processing will allow for SAR data to be rapidly brought into a common reference frame for immediate exploitation and data fusion with other three-dimensional data and representations.

Keywords: backprojection, data fusion, exploitation, three-dimensional, visualization

Procedia PDF Downloads 86

Authors: M. S. Wickramarachchi, L. S. Nawarathna, C. M. B. Dematawewa

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Hatchery performance is critical for the profitability of poultry breeder operations. Some extrinsic parameters of eggs and breeders cause to increase or decrease the hatchability. This study aims to identify the affecting extrinsic parameters on the commercial hatchability of local chicken's eggs and determine the most efficient classification model with a hatchability rate greater than 90%. In this study, seven extrinsic parameters were considered: egg weight, moisture loss, breeders age, number of fertilised eggs, shell width, shell length, and shell thickness. Multiple linear regression was performed to determine the most influencing variable on hatchability. First, the correlation between each parameter and hatchability were checked. Then a multiple regression model was developed, and the accuracy of the fitted model was evaluated. Linear Discriminant Analysis (LDA), Classification and Regression Trees (CART), k-Nearest Neighbors (kNN), Support Vector Machines (SVM) with a linear kernel, and Random Forest (RF) algorithms were applied to classify the hatchability. This grouping process was conducted using binary classification techniques. Hatchability was negatively correlated with egg weight, breeders' age, shell width, shell length, and positive correlations were identified with moisture loss, number of fertilised eggs, and shell thickness. Multiple linear regression models were more accurate than single linear models regarding the highest coefficient of determination (R²) with 94% and minimum AIC and BIC values. According to the classification results, RF, CART, and kNN had performed the highest accuracy values 0.99, 0.975, and 0.972, respectively, for the commercial hatchery process. Therefore, the RF is the most appropriate machine learning algorithm for classifying the breeder outcomes, which are economically profitable or not, in a commercial hatchery.

Keywords: classification models, egg weight, fertilised eggs, multiple linear regression

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Authors: Mohammed Sabbah, Asmaa Al-Asmar, Doaa Abu-Hani, Fuad Al-Rimawi

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Plant extracts are an increasingly popular natural product with a wide range of potential applications in food, industrial, and health care industries. They are rich in polyphenolic compounds and flavonoids, which have been demonstrated to possess a variety of beneficial properties, including antimicrobial and antioxidant activity. Plant extracts have been found to possess antimicrobial activity against a variety of foodborne pathogens and can be used as a natural preservative to extend the shelf life of food products. They have also strong antioxidant activity, which can reduce the formation of free radicals and oxidation of food components. Recently there is an increase interest in bio-based polymers to be used as innovative “bioplastics” for industrial exploitation e.g. packaging materials for food products. Additionally, incorporation of active compounds (e.g. antioxidants and antimicrobials) in bio-polymer materials is of particular interest since such active polymers can be used as active packaging materials (with antimicrobial and antioxidant activity). In this work, different plant extracts have been characterized for their phenolic compounds, flavonoids content, antioxidant activity (both as free radical scavenging ability and reducing ability), and antimicrobial activity against gram positive and negative bacteria (Escherichia coli; Staphylococcus aureus, and Pseudomonas aeruginosa) as well as antifungal activities (against yeast, mold and Botrytis cinera/a plant pathogen). Results showed that many extracts are rich with polyphenolic compounds and flavonoids and have strong antioxidant activities, and rich with phytochemicals (e.g. rutin, quercetin, oleuropein, tyrosol and hydroxytyrosol). Some extracts showed antibacterial activity against both gram positive and negative bacteria as well as antifungal activities and can work, therefore, as preservatives for food or pharmaceutical industries. As an application, two extracts were used as additive to pectin-based packaging film, and results showed that the addition of these extracts significantly improve their functionality as antimicrobial and antioxidant activity. These biomaterials, therefore can be used in food packaging materials to extend the shelf life of food products.

Keywords: plant extracts, antioxidants, flavonoids, bioplastic, edible biofilm, packaging materials

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Authors: Onyinyechi Joy Kingdom

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Mergers and Acquisitions (M&A) have been proposed as a mechanism through which, problems associated with inefficiency or poor performance in financial institution could be addressed. The aim of this study is to examine the proposition that recapitalization of banks, which encouraged Mergers and Acquisitions in Nigeria banking system, would strengthen the domestic banks, improve financial deepening and the confidence of depositors. Hence, this study examines the impact of the 2005 M&A in the Nigerian-banking sector on financial deepening using mixed method (quantitative and qualitative approach). The quantitative process of this study utilised annual time series for financial deepening indicator for the period of 1997 to 2012. While, the qualitative aspect adopted semi-structured interview to collate data from three merged banks and three stand-alone banks to explore, understand and complement the quantitative results. Furthermore, a framework thematic analysis is employed to analyse the themes developed using NVivo 11 software. Using the quantitative approach, findings from the equality of mean test (EMT) used suggests that M&A have significant impact on financial deepening. However, this method is not robust enough given its weak validity as it does not control for other potential factors that may determine financial deepening. Thus, to control for other factors that may affect the level of financial deepening, a Multiple Regression Model (MRM) and Interrupted Times Series Analysis (ITSA) were applied. The coefficient for M&A dummy turned negative and insignificant using MRM. In addition, the estimated linear trend of the post intervention when ITSA was applied suggests that after M&A, the level of financial deepening decreased annually; however, this was statistically insignificant. Similarly, using the qualitative approach, the results from the interview supported the quantitative results from ITSA and MRM. The result suggests that interest rate should fall when capital base is increased to improve financial deepening. Hence, this study contributes to the existing literature the importance of other factors that may affect financial deepening and the economy when policies that will enhance bank performance and the economy are made. In addition, this study will enable the use of valuable policy instruments relevant to monetary authorities when formulating policies that will strengthen the Nigerian banking sector and the economy.

Keywords: mergers and acquisitions, recapitalization, financial deepening, efficiency, financial crisis

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Authors: Kaiwen Zheng, Wanting Zhou, Nan Tang, Lei Li, Yuanhang He

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The integrated circuit industry has become a cornerstone of the information society, finding widespread application in areas such as industry, communication, medicine, and aerospace. However, with the increasing complexity of integrated circuits, Hardware Trojans (HTs) implanted by attackers have become a significant threat to their security. In this paper, we proposed a hardware trojan detection method for large-scale circuits. As HTs introduce physical characteristic changes such as structure, area, and power consumption as additional redundant circuits, we proposed a machine-learning-based hardware trojan detection method based on the physical characteristics of gate-level netlists. This method transforms the hardware trojan detection problem into a machine-learning binary classification problem based on physical characteristics, greatly improving detection speed. To address the problem of imbalanced data, where the number of pure circuit samples is far less than that of HTs circuit samples, we used the SMOTETomek algorithm to expand the dataset and further improve the performance of the classifier. We used three machine learning algorithms, K-Nearest Neighbors, Random Forest, and Support Vector Machine, to train and validate benchmark circuits on Trust-Hub, and all achieved good results. In our case studies based on AES encryption circuits provided by trust-hub, the test results showed the effectiveness of the proposed method. To further validate the method’s effectiveness for detecting variant HTs, we designed variant HTs using open-source HTs. The proposed method can guarantee robust detection accuracy in the millisecond level detection time for IC, and FPGA design flows and has good detection performance for library variant HTs.

Keywords: hardware trojans, physical properties, machine learning, hardware security

Procedia PDF Downloads 147

Authors: Amin Naboureh, Ainong Li, Jinhu Bian, Guangbin Lei, Hamid Ebrahimy

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Large-scale land cover mapping has become a new challenge in land change and remote sensing field because of involving a big volume of data. Moreover, selecting the right classification method, especially when there are different types of landscapes in the study area is quite difficult. This paper is an attempt to compare the performance of different machine learning (ML) algorithms for generating a land cover map of the China-Central Asia–West Asia Corridor that is considered as one of the main parts of the Belt and Road Initiative project (BRI). The cloud-based Google Earth Engine (GEE) platform was used for generating a land cover map for the study area from Landsat-8 images (2017) by applying three frequently used ML algorithms including random forest (RF), support vector machine (SVM), and artificial neural network (ANN). The selected ML algorithms (RF, SVM, and ANN) were trained and tested using reference data obtained from MODIS yearly land cover product and very high-resolution satellite images. The finding of the study illustrated that among three frequently used ML algorithms, RF with 91% overall accuracy had the best result in producing a land cover map for the China-Central Asia–West Asia Corridor whereas ANN showed the worst result with 85% overall accuracy. The great performance of the GEE in applying different ML algorithms and handling huge volume of remotely sensed data in the present study showed that it could also help the researchers to generate reliable long-term land cover change maps. The finding of this research has great importance for decision-makers and BRI’s authorities in strategic land use planning.

Keywords: land cover, google earth engine, machine learning, remote sensing

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Authors: Alvarado M. Lady G., Ortiz V. Yaylenne, Quintero B. Quelbis R.

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The oil palm sector has seen significant growth in Colombia after the insertion of policies to stimulate the use of biofuels, which eventually contributes to the reduction of greenhouse gases (GHG) that deteriorate not only the environment but the health of people. However, the policy of using biofuels has been strongly questioned by the impacts that can generate; an example is the increase of other more harmful GHGs like the CH₄ that underlies the amount of solid waste generated. Casanare's department is estimated be one of the major producers of palm oil of the country given that has recently expanded its sowed area, which implies an increase in waste generated primarily in the industrial stage. For this reason, the following study evaluated the agronomic potential of the biochar obtained from empty fruit bunches and its nutritional contribution in cultivated soils with Elaeis guineensis in Casanare, Colombia. The biochar was obtained by slow pyrolysis of the clusters in a retort oven at an average temperature of 190 °C and a residence time of 8 hours. The final product was taken to the laboratory for its physical and chemical analysis as well as a soil sample from a cultivation of Elaeis guineensis located in Tauramena-Casanare. With the results obtained plus the bibliographical reports of the nutrient demand in this cultivation, the possible nutritional contribution of the biochar was determined. It is estimated that the cultivation requirements of nitrogen is 12.1 kg.ha⁻¹, potassium is 59.3 kg.ha⁻¹, magnesium is -31.5 kg.ha⁻¹ and phosphorus is 5.6 kg.ha⁻¹ obtaining a biochar contribution of 143.1 kg.ha⁻¹, 1204.5 kg.ha⁻¹, 39.2 kg.ha⁻¹ and 71.6 kg.ha⁻¹ respectively. The incorporation of biochar into the soil would significantly improve the concentrations of N, P, K and Mg, nutrients considered important in the yield of palm oil, coupled with the importance of nutrient recycling in agricultural production systems sustainable. The biochar application improves the physical properties of soils, mainly in the humidity retention. On the other hand, it regulates the availability of nutrients for plants absorption, with economic savings in the application of synthetic fertilizers and water by irrigation. It also becomes an alternative to manage agricultural waste, reducing the involuntary emissions of greenhouse gases to the environment by decomposition in the field, reducing the CO₂ content in the atmosphere.

Keywords: biochar, nutrient recycling, oil palm, pyrolysis

Procedia PDF Downloads 157