Search results for: linear predictive coding (LPC)

Authors: Alexander Cho, Harsho Sanyal, Joseph Cataldo

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A quantitative analysis of the different variables on both the South and North green roofs of the Jacob K. Javits Convention Center was taken to find mathematical relationships between net radiation and evapotranspiration (ET), average outside temperature, and the lysimeter weight. Groups of datasets were analyzed, and the relationships were plotted on linear and semi-log graphs to find consistent relationships. Antecedent conditions for each rainstorm were also recorded and plotted against the volumetric water difference within the lysimeter. The first relation was the inverse parabolic relationship between the lysimeter weight and the net radiation and ET. The peaks and valleys of the lysimeter weight corresponded to valleys and peaks in the net radiation and ET respectively, with the 8/22/15 and 1/22/16 datasets showing this trend. The U-shaped and inverse U-shaped plots of the two variables coincided, indicating an inverse relationship between the two variables. Cross variable relationships were examined through graphs with lysimeter weight as the dependent variable on the y-axis. 10 out of 16 of the plots of lysimeter weight vs. outside temperature plots had R² values > 0.9. Antecedent conditions were also recorded for rainstorms, categorized by the amount of precipitation accumulating during the storm. Plotted against the change in the volumetric water weight difference within the lysimeter, a logarithmic regression was found with large R² values. The datasets were compared using the Mann Whitney U-test to see if the datasets were statistically different, using a significance level of 5%; all datasets compared showed a U test statistic value, proving the null hypothesis of the datasets being different from being true.

Keywords: green roof, green infrastructure, Javits Center, evapotranspiration, net radiation, lysimeter

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Authors: Antonela Matana, Dubravka Brdar, Vesela Torlak, Marijana Popovic, Ivana Gunjaca, Ozren Polasek, Vesna Boraska Perica, Maja Barbalic, Ante Punda, Caroline Hayward, Tatijana Zemunik

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Parathyroid hormone (PTH) plays a critical role in the regulation of bone mineral metabolism and calcium homeostasis. Higher PTH levels are associated with heart failure, hypertension, coronary artery disease, cardiovascular mortality and poorer bone health. A twin study estimated that 60% of the variation in PTH concentrations is genetically determined. Only one GWAS of PTH concentration has been reported to date. Identified loci explained 4.5% of the variance in circulating PTH, suggesting that additional genetic variants remain undiscovered. Therefore, the aim of this study was to identify novel genetic variants associated with PTH levels in a general population. We have performed a GWAS meta-analysis on 2596 individuals originating from three Croatian cohorts: City of Split and the Islands of Korčula and Vis, within a large-scale project of “10,001 Dalmatians”. A total of 7 411 206 variants, imputed using the 1000 Genomes reference panel, with minor allele frequency ≥ 1% and Rsq ≥ 0.5 were analyzed for the association. GWAS within each data set was performed under an additive model, controlling for age, gender and relatedness. Meta-analysis was conducted using the inverse-variance fixed-effects method. Furthermore, to identify sex-specific effects, we have conducted GWAS meta-analyses analyzing males and females separately. In addition, we have performed biological pathway analysis. Four SNPs, representing one locus, reached genome-wide significance. The most significant SNP was rs11099476 on chromosome 4 (P=1.15x10-8), which explained 1.14 % of the variance in PTH. The SNP is located near the protein-coding gene RASGEF1B. Additionally, we detected suggestive association with SNPs, rs77178854 located on chromosome 2 in the DPP10 gene (P=2.46x10-7) and rs481121 located on chromosome 1 (P=3.58x10-7) near the GRIK1 gene. One of the top hits detected in the main meta-analysis, intron variant rs77178854 located within DPP10 gene, reached genome-wide significance in females (P=2.21x10-9). No single locus was identified in the meta-analysis in males. Fifteen biological pathways were functionally enriched at a P<0.01, including muscle contraction, ion homeostasis and cardiac conduction as the most significant pathways. RASGEF1B is the guanine nucleotide exchange factor, known to be associated with height, bone density, and hip. DPP10 encodes a membrane protein that is a member of the serine proteases family, which binds specific voltage-gated potassium channels and alters their expression and biophysical properties. In conclusion, we identified 2 novel loci associated with PTH levels in a general population, providing us with further insights into the genetics of this complex trait.

Keywords: general population, genome-wide association analysis, parathyroid hormone, single nucleotide polymorphisms.

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Authors: Mohammed-Hareef Asunramu, Sana Hashemi, Raja Ohri, Luc Worthington, Nadia Zaman, Junkai Zhu

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Background: The 2012 Health and Social Care Act committed to a ‘parity of esteem between mental and physical health services. Although this investment, aimed to both increase the quality of services and ensure the retention of mental health staff, questions remained regarding its ability to prevent mental health problems. One possible solution is a focus on the social determinants of health which have been shown to impact mental health. Aim: To examine the relationship between the index of multiple deprivations (IMD) and the prevalence of common mental health disorders (CMD) for CCGs in NHS England between 2019 and 2020. Design and setting: Cross-sectional analysis of 189 CCGs in NHS England. Methods: A multivariate linear regression model was utilized with CMD as outcome variable and IMD, age and ethnicity as explanatory variables. Datasets were obtained from Public Health England and the latest UK Census. Results: CCG IMD was found to have a significantly positive relationship with CMD. For every 1-point increase in IMD, CMD increases by 0.25%. Ethnicity had a significantly positive relationship with CMD. For every 1% increase in the population that identifies as BME, there is a 0.03% increase in CMD. Age had a significantly negative relationship with CMD. For every 1% increase in the population aged 60+, there is a 0.11% decrease in CMD. Conclusion: This study demonstrates that addressing mental health issues may require a multi-pronged approach. Beyond budget increases, it is essential to prioritize health equity, with careful considerations towards ethnic minorities and different age brackets.

Keywords: deprivation, health inequality, mental health, social determinants

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Authors: Mohammad A. Bani-Khaled

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In this work we use the Discrete Proper Orthogonal Decomposition transform to characterize the properties of coupled dynamics in thin-walled beams by exploiting numerical simulations obtained from finite element simulations. The outcomes of the will improve our understanding of the linear and nonlinear coupled behavior of thin-walled beams structures. Thin-walled beams have widespread usage in modern engineering application in both large scale structures (aeronautical structures), as well as in nano-structures (nano-tubes). Therefore, detailed knowledge in regard to the properties of coupled vibrations and buckling in these structures are of great interest in the research community. Due to the geometric complexity in the overall structure and in particular in the cross-sections it is necessary to involve computational mechanics to numerically simulate the dynamics. In using numerical computational techniques, it is not necessary to over simplify a model in order to solve the equations of motions. Computational dynamics methods produce databases of controlled resolution in time and space. These numerical databases contain information on the properties of the coupled dynamics. In order to extract the system dynamic properties and strength of coupling among the various fields of the motion, processing techniques are required. Time- Proper Orthogonal Decomposition transform is a powerful tool for processing databases for the dynamics. It will be used to study the coupled dynamics of thin-walled basic structures. These structures are ideal to form a basis for a systematic study of coupled dynamics in structures of complex geometry.

Keywords: coupled dynamics, geometric complexity, proper orthogonal decomposition (POD), thin walled beams

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Authors: Mehdi Boukri, Mohammed Naboussi Farsi, Mounir Naili, Omar Amellal, Mohamed Belazougui, Ahmed Mebarki, Nabila Guessoum, Brahim Mezazigh, Mounir Ait-Belkacem, Nacim Yousfi, Mohamed Bouaoud, Ikram Boukal, Aboubakr Fettar, Asma Souki

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The seismic risk to which the urban centres are more and more exposed became a world concern. A co-operation on an international scale is necessary for an exchange of information and experiments for the prevention and the installation of action plans in the countries prone to this phenomenon. For that, the 1990s was designated as 'International Decade for Natural Disaster Reduction (IDNDR)' by the United Nations, whose interest was to promote the capacity to resist the various natural, industrial and environmental disasters. Within this framework, it was launched in 1996, the RADIUS project (Risk Assessment Tools for Diagnosis of Urban Areas Against Seismic Disaster), whose the main objective is to mitigate seismic risk in developing countries, through the development of a simple and fast methodological and operational approach, allowing to evaluate the vulnerability as well as the socio-economic losses, by probable earthquake scenarios in the exposed urban areas. In this paper, we will present the adaptation and application of this methodology to the Algerian context for the seismic risk evaluation in urban areas potentially exposed to earthquakes. This application consists to perform an earthquake scenario in the urban centre of Constantine city, located at the North-East of Algeria, which will allow the building seismic damage estimation of this city. For that, an inventory of 30706 building units was carried out by the National Earthquake Engineering Research Centre (CGS). These buildings were digitized in a data base which comprises their technical information by using a Geographical Information system (GIS), and then they were classified according to the RADIUS methodology. The study area was subdivided into 228 meshes of 500m on side and Ten (10) sectors of which each one contains a group of meshes. The results of this earthquake scenario highlights that the ratio of likely damage is about 23%. This severe damage results from the high concentration of old buildings and unfavourable soil conditions. This simulation of the probable seismic damage of the building and the GIS damage maps generated provide a predictive evaluation of the damage which can occur by a potential earthquake near to Constantine city. These theoretical forecasts are important for decision makers in order to take the adequate preventive measures and to develop suitable strategies, prevention and emergency management plans to reduce these losses. They can also help to take the adequate emergency measures in the most impacted areas in the early hours and days after an earthquake occurrence.

Keywords: seismic risk, mitigation, RADIUS, urban areas, Algeria, earthquake scenario, Constantine

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Authors: Ka C. Lam, Oluwafunmibi S. Idowu

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Uncertainty in cost is a major factor affecting performance of construction projects. To our knowledge, several conceptual cost models have been developed with varying degrees of accuracy. Incorporating conceptual quantities into conceptual cost models could improve the accuracy of early predesign cost estimates. Hence, the development of quantity models for estimating conceptual quantities of framed reinforced concrete structures using supervised machine learning is the aim of the current research. Using measured quantities of structural elements and design variables such as live loads and soil bearing pressures, response and predictor variables were defined and used for constructing conceptual quantities models. Twenty-four models were developed for comparison using a combination of non-parametric support vector regression, linear regression, and bootstrap resampling techniques. R programming language was used for data analysis and model implementation. Gross soil bearing pressure and gross floor loading were discovered to have a major influence on the quantities of concrete and reinforcement used for foundations. Building footprint and gross floor loading had a similar influence on beams and slabs. Future research could explore the modelling of other conceptual quantities for walls, finishes, and services using machine learning techniques. Estimation of conceptual quantities would assist construction planners in early resource planning and enable detailed performance evaluation of early cost predictions.

Keywords: bootstrapping, conceptual quantities, modelling, reinforced concrete, support vector regression

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Authors: Tien S. H. Pham, Peter J. Mahon, Aimin Yu

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The detection of drug molecules by voltammetry has attracted great interest over the past years. However, many drug molecules exhibit poor electrochemical signals at common electrodes which result in low sensitivity in detection. An efficient way to overcome this problem is to modify electrodes with functional materials. Since discovered in 2004, graphene (or reduced graphene oxide) has emerged as one of the most studied two-dimensional carbon materials in condensed matter physics, electrochemistry, and so on due to its exceptional physicochemical properties. Additionally, the continuous development of technology has opened the new window for the successful fabrications of many novel graphene-based nanomaterials to serve in electrochemical analysis. This research aims to synthesize and characterize gold nanoparticle coated beta-cyclodextrin functionalized reduced graphene oxide (Au NP–β-CD–RGO) nanocomposites with highly conductive and strongly electro-catalytic properties as well as excellent supramolecular recognition abilities for the modification of electrodes. The electrochemical responses of ciprofloxacin at the as-prepared nanocomposite modified electrode was effectively amplified was much higher in comparison with that at the bare electrode. The linear concentration range was from 0.01 to 120 µM, with a detection limit of 2.7 nM using differential pulse voltammetry. Thus, Au NP–β-CD–RGO nanocomposite has great potential as an ideal material to construct sensitive sensors for the electrochemical determination of ciprofloxacin or similar antibacterial drugs in the future based on its excellent stability, selectivity, and reproducibility.

Keywords: Au nanoparticles, β-CD, ciprofloxacin, electrochemical determination, graphene based nanomaterials

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Authors: Fajana Sola Isaac

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In the last two decades, we have witnessed an increased interest in exchange rate pass-through (ERPT) in developing economies and emerging markets. This is perhaps due to the acknowledged significance of the pattern of exchange rate pass-through as a key instrument in monetary policy design, principally in retort to a shock in exchange rate in literature. This paper analyzed Exchange Rate Pass-Through by A Model of Oil Prices and Asymmetric Exchange Rate Fluctuations in Selected African Countries. The study adopted A Non-Linear Autoregressive Distributed Lag approach using yearly data on Algeria, Burundi, Nigeria and South Africa from 1986 to 2022. The paper found asymmetry in exchange rate pass-through in net oil-importing and net oil-exporting countries in the short run during the period under review. An ERPT exhibited a complete pass-through in the short run in the case of net oil-importing countries but an incomplete pass-through in the case of the net oil-exporting countries that were examined. An extended result revealed a significant impact of oil price shock on exchange rate pass-through to domestic price in the long run only for net oil importing countries. The Wald restriction test also confirms the evidence of asymmetric with the role of oil price acting as an accelerator to exchange rate pass-through to domestic price in the countries examined. The study found the outcome to be very useful for gaining expansive knowledge on the external shock impact on ERPT and could be of critical value for national monetary policy decisions on inflation targeting, especially for countries examined and other developing net oil importers and exporters.

Keywords: pass through, exchange rate, ARDL, monetary policy

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Authors: Benaliouche Hana, Abdessemed Djamal, Meniai Abdessalem, Lesage Geoffroy, Heran Marc

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This paper presents a dynamic mathematical model of activated sludge which is able to predict the formation and degradation kinetics of SMP (Soluble microbial products) in membrane bioreactor systems. The model is based on a calibrated version of ASM1 with the theory of production and degradation of SMP. The model was calibrated on the experimental data from MBR (Mathematical modeling Membrane bioreactor) pilot plant. Analytical expressions have been developed, describing the concentrations of the main state variables present in the sludge matrix, with the inclusion of only six additional linear differential equations. The objective is to present a new dynamic mathematical model of activated sludge capable of predicting the formation and degradation kinetics of SMP (UAP and BAP) from the submerged membrane bioreactor (BRMI), operating at low organic load (C / N = 3.5), for two sludge retention times (SRT) fixed at 40 days and 60 days, to study their impact on membrane fouling, The modeling study was carried out under the steady-state condition. Analytical expressions were then validated by comparing their results with those obtained by simulations using GPS-X-Hydromantis software. These equations made it possible, by means of modeling approaches (ASM1), to identify the operating and kinetic parameters and help to predict membrane fouling.

Keywords: Activated Sludge Model No. 1 (ASM1), mathematical modeling membrane bioreactor, soluble microbial products, UAP, BAP, Modeling SMP, MBR, heterotrophic biomass

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Authors: Raana Babadi Fathipour

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Tea, second only to water in global consumption rates, holds a significant place as the beverage of choice for many around the world. The process of fermenting tea leaves plays a crucial role in determining its ultimate quality, traditionally assessed through meticulous observation by tea tasters and laboratory analysis. However, advancements in technology have paved the way for innovative electronic sensing platforms like the electronic nose (e-nose), electronic tongue (e-tongue), and electronic eye (e-eye). These cutting-edge tools, coupled with sophisticated data processing algorithms, not only expedite the assessment of tea's sensory qualities based on consumer preferences but also establish new benchmarks for this esteemed bioactive product to meet burgeoning market demands worldwide. By harnessing intricate data sets derived from electronic signals and deploying multivariate statistical techniques, these technological marvels can enhance accuracy in predicting and distinguishing tea quality with unparalleled precision. In this contemporary exploration, a comprehensive overview is provided of the most recent breakthroughs and viable solutions aimed at addressing forthcoming challenges in the realm of tea analysis. Utilizing bio-mimicking Electronic Sensory Perception systems (ESPs), researchers have developed innovative technologies that enable precise and instantaneous evaluation of the sensory-chemical attributes inherent in tea and its derivatives. These sophisticated sensing mechanisms are adept at deciphering key elements such as aroma, taste, and color profiles, transitioning valuable data into intricate mathematical algorithms for classification purposes. Through their adept capabilities, these cutting-edge devices exhibit remarkable proficiency in discerning various teas with respect to their distinct pricing structures, geographic origins, harvest epochs, fermentation processes, storage durations, quality classifications, and potential adulteration levels. While voltammetric and fluorescent sensor arrays have emerged as promising tools for constructing electronic tongue systems proficient in scrutinizing tea compositions, potentiometric electrodes continue to serve as reliable instruments for meticulously monitoring taste dynamics within different tea varieties. By implementing a feature-level fusion strategy within predictive models, marked enhancements can be achieved regarding efficiency and accuracy levels. Moreover, by establishing intrinsic linkages through pattern recognition methodologies between sensory traits and biochemical makeup found within tea samples, further strides are made toward enhancing our understanding of this venerable beverage's complex nature.

Keywords: classifier system, tea, polyphenol, sensor, taste sensor

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Authors: Julia Ive, Lucia Specia

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Understanding and detecting toxicity is an important problem to support safer human interactions online. Our work focuses on the important problem of contextual toxicity detection, where automated classifiers are tasked with determining whether a short textual segment (usually a sentence) is toxic within its conversational context. We use “toxicity” as an umbrella term to denote a number of variants commonly named in the literature, including hate, abuse, offence, among others. Detecting toxicity in context is a non-trivial problem and has been addressed by very few previous studies. These previous studies have analysed the influence of conversational context in human perception of toxicity in controlled experiments and concluded that humans rarely change their judgements in the presence of context. They have also evaluated contextual detection models based on state-of-the-art Deep Learning and Natural Language Processing (NLP) techniques. Counterintuitively, they reached the general conclusion that computational models tend to suffer performance degradation in the presence of context. We challenge these empirical observations by devising better contextual predictive models that also rely on NLP data augmentation techniques to create larger and better data. In our study, we start by further analysing the human perception of toxicity in conversational data (i.e., tweets), in the absence versus presence of context, in this case, previous tweets in the same conversational thread. We observed that the conclusions of previous work on human perception are mainly due to data issues: The contextual data available does not provide sufficient evidence that context is indeed important (even for humans). The data problem is common in current toxicity datasets: cases labelled as toxic are either obviously toxic (i.e., overt toxicity with swear, racist, etc. words), and thus context does is not needed for a decision, or are ambiguous, vague or unclear even in the presence of context; in addition, the data contains labeling inconsistencies. To address this problem, we propose to automatically generate contextual samples where toxicity is not obvious (i.e., covert cases) without context or where different contexts can lead to different toxicity judgements for the same tweet. We generate toxic and non-toxic utterances conditioned on the context or on target tweets using a range of techniques for controlled text generation(e.g., Generative Adversarial Networks and steering techniques). On the contextual detection models, we posit that their poor performance is due to limitations on both of the data they are trained on (same problems stated above) and the architectures they use, which are not able to leverage context in effective ways. To improve on that, we propose text classification architectures that take the hierarchy of conversational utterances into account. In experiments benchmarking ours against previous models on existing and automatically generated data, we show that both data and architectural choices are very important. Our model achieves substantial performance improvements as compared to the baselines that are non-contextual or contextual but agnostic of the conversation structure.

Keywords: contextual toxicity detection, data augmentation, hierarchical text classification models, natural language processing

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Authors: Yiea Funk Te, Daniel Mueller, Irena Pletikosa Cvijikj

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Small and medium enterprises (SMEs) play an important role in the economy of many countries. When the overall world economy is considered, SMEs represent 95% of all businesses in the world, accounting for 66% of the total employment. Existing studies show that the current business environment is characterized as highly turbulent and strongly influenced by modern information and communication technologies, thus forcing SMEs to experience more severe challenges in maintaining their existence and expanding their business. To support SMEs at improving their competitiveness, researchers recently turned their focus on applying data mining techniques to build risk and growth prediction models. However, data used to assess risk and growth indicators is primarily obtained via questionnaires, which is very laborious and time-consuming, or is provided by financial institutes, thus highly sensitive to privacy issues. Recently, web mining (WM) has emerged as a new approach towards obtaining valuable insights in the business world. WM enables automatic and large scale collection and analysis of potentially valuable data from various online platforms, including companies’ websites. While WM methods have been frequently studied to anticipate growth of sales volume for e-commerce platforms, their application for assessment of SME risk and growth indicators is still scarce. Considering that a vast proportion of SMEs own a website, WM bears a great potential in revealing valuable information hidden in SME websites, which can further be used to understand SME risk and growth indicators, as well as to enhance current SME risk and growth prediction models. This study aims at developing an automated system to collect business-relevant data from the Web and predict future growth trends of SMEs by means of WM and data mining techniques. The envisioned system should serve as an 'early recognition system' for future growth opportunities. In an initial step, we examine how structured and semi-structured Web data in governmental or SME websites can be used to explain the success of SMEs. WM methods are applied to extract Web data in a form of additional input features for the growth prediction model. The data on SMEs provided by a large Swiss insurance company is used as ground truth data (i.e. growth-labeled data) to train the growth prediction model. Different machine learning classification algorithms such as the Support Vector Machine, Random Forest and Artificial Neural Network are applied and compared, with the goal to optimize the prediction performance. The results are compared to those from previous studies, in order to assess the contribution of growth indicators retrieved from the Web for increasing the predictive power of the model.

Keywords: data mining, SME growth, success factors, web mining

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Authors: Vildan Kistik, Tuncay Can

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From a business perspective, cost and profit are two key factors for businesses. The intent of most businesses is to minimize the cost to maximize or equalize the profit, so as to provide the greatest benefit to itself. However, the physical system is very complicated because of technological constructions, rapid increase of competitive environments and similar factors. In such a system it is not easy to maximize profits or to minimize costs. Businesses must decide on the competence and competence of the personnel to be recruited, taking into consideration many criteria in selecting personnel. There are many criteria to determine the competence and competence of a staff member. Factors such as the level of education, experience, psychological and sociological position, and human relationships that exist in the field are just some of the important factors in selecting a staff for a firm. Personnel selection is a very important and costly process in terms of businesses in today's competitive market. Although there are many mathematical methods developed for the selection of personnel, unfortunately the use of these mathematical methods is rarely encountered in real life. In this study, unlike other methods, an exponential programming model was established based on the possibilities of failing in case the selected personnel was started to work. With the necessary transformations, the problem has been transformed into unconstrained Geometrical Programming problem and personnel selection problem is approached with geometric programming technique. Personnel selection scenarios for a classroom were established with the help of normal distribution and optimum solutions were obtained. In the most appropriate solutions, the personnel selection process for the classroom has been achieved with minimum cost.

Keywords: geometric programming, personnel selection, non-linear programming, operations research

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Authors: Bernardo Cortez, Florian Peter, Thomas Lausenhammer, Paulo Oliveira

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Designing missiles’ autopilot controllers have been a complex task, given the extensive flight envelope and the nonlinear flight dynamics. A solution that can excel both in nominal performance and in robustness to uncertainties is still to be found. While Control Theory often debouches into parameters’ scheduling procedures, Reinforcement Learning has presented interesting results in ever more complex tasks, going from videogames to robotic tasks with continuous action domains. However, it still lacks clearer insights on how to find adequate reward functions and exploration strategies. To the best of our knowledge, this work is a pioneer in proposing Reinforcement Learning as a framework for flight control. In fact, it aims at training a model-free agent that can control the longitudinal non-linear flight dynamics of a missile, achieving the target performance and robustness to uncertainties. To that end, under TRPO’s methodology, the collected experience is augmented according to HER, stored in a replay buffer and sampled according to its significance. Not only does this work enhance the concept of prioritized experience replay into BPER, but it also reformulates HER, activating them both only when the training progress converges to suboptimal policies, in what is proposed as the SER methodology. The results show that it is possible both to achieve the target performance and to improve the agent’s robustness to uncertainties (with low damage on nominal performance) by further training it in non-nominal environments, therefore validating the proposed approach and encouraging future research in this field.

Keywords: Reinforcement Learning, flight control, HER, missile autopilot, TRPO

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Authors: Fouzieh Rouzmehr, Mehdi Mousavi

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Hot mix asphalt concrete (HMAC) is a mixture of aggregates and bitumen. The primary ingredient that determines the mechanical properties of HMAC is the bitumen in it, which displays viscoelastic behavior under normal service conditions. For simplicity, asphalt concrete is considered an elastic material, but this is far from reality at high service temperatures and longer loading times. Viscoelasticity means that the material's stress-strain relationship depends on the strain rate and loading duration. The goal of this paper is to simulate the mechanical response of flexible pavements using linear elastic and viscoelastic modeling of asphalt concrete and predict pavement performance. Falling Weight Deflectometer (FWD) load will be simulated and the results for elastic and viscoelastic modeling will be evaluated. The viscoelastic simulation is performed by the Prony series, which will be modeled by using ANSYS software. Inflexible pavement design, tensile strain at the bottom of the surface layer and compressive strain at the top of the last layer plays an important role in the structural response of the pavement and they will imply the number of loads for fatigue (Nf) and rutting (Nd) respectively. The differences of these two modelings are investigated on fatigue cracking and rutting problem, which are the two main design parameters in flexible pavement design. Although the differences in rutting problem between the two models were negligible, in fatigue cracking, the viscoelastic model results were more accurate. Results indicate that modeling the flexible pavement with elastic material is efficient enough and gives acceptable results.

Keywords: flexible pavement, asphalt, FEM, viscoelastic, elastic, ANSYS, modeling

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Authors: Rakesh Kumar, Fatima Electricwala

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One of the major thrusts of the Bus Rapid Transit System is to reduce the commuter’s dependency on private vehicles and increase the shares of public transport to make urban transportation system environmentally sustainable. In this study, commuter mode choice analysis is performed that examines behavioral responses to the proposed Bus Rapid Transit System (BRTS) in Surat, with estimation of the probable shift from private mode to public mode. Further, evaluation of the BRTS scenarios, using Surat’s transportation ecological footprint was done. A multi-modal simulation model was developed in Biogeme environment to explicitly consider private users behaviors and non-linear environmental impact. The data of the different factors (variables) and its impact that might cause modal shift of private mode users to proposed BRTS were collected through home-interview survey using revealed and stated preference approach. A multi modal logit model of mode-choice was then calibrated using the collected data and validated using proposed sample. From this study, a set of perception factors, with reliable and predictable data base, to explain the variation in modal shift behaviour and their impact on Surat’s ecological environment has been identified. A case study of the proposed BRTS connecting the Surat Industrial Hub to the coastal area is provided to illustrate the approach.

Keywords: BRTS, private modes, mode choice models, ecological footprint

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Authors: Matthias Haenisch, Marc Godau, Julia Barreiro, Dominik Maxelon

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In times of ubiquitous digitalization and the increasing entanglement of humans and technologies in musical practices in the 21st century, it is to be asked, how popular musicians learn in the (post)digital Age. Against the backdrop of the increasing interest in transferring informal learning practices into formal settings of music education the interdisciplinary research association »MusCoDA – Musical Communities in the (Post)Digital Age« (University of Erfurt/University of Applied Sciences Clara Hoffbauer Potsdam, funded by the German Ministry of Education and Research, pursues the goal to derive an empirical model of collective songwriting practices from the study of informal lelearningf songwriters and bands that can be translated into pedagogical concepts for music education in schools. Drawing on concepts from Community of Musical Practice and Actor Network Theory, lelearnings considered not only as social practice and as participation in online and offline communities, but also as an effect of heterogeneous networks composed of human and non-human actors. Learning is not seen as an individual, cognitive process, but as the formation and transformation of actor networks, i.e., as a practice of assembling and mediating humans and technologies. Based on video stimulated recall interviews and videography of online and offline activities, songwriting practices are followed from the initial idea to different forms of performance and distribution. The data evaluation combines coding and mapping methods of Grounded Theory Methodology and Situational Analysis. This results in network maps in which both the temporality of creative practices and the material and spatial relations of human and technological actors are reconstructed. In addition, positional analyses document the power relations between the participants that structure the learning process of the field. In the area of online informal lelearninginitial key research findings reveal a transformation of the learning subject through the specific technological affordances of TikTok and Instagram and the accompanying changes in the learning practices of the corresponding online communities. Learning is explicitly shaped by the material agency of online tools and features and the social practices entangled with these technologies. Thus, any human online community member can be invited to directly intervene in creative decisions that contribute to the further compositional and structural development of songs. At the same time, participants can provide each other with intimate insights into songwriting processes in progress and have the opportunity to perform together with strangers and idols. Online Lelearnings characterized by an increase in social proximity, distribution of creative agency and informational exchange between participants. While it seems obvious that traditional notions not only of lelearningut also of the learning subject cannot be maintained, the question arises, how exactly the observed informal learning practices and the subject that emerges from the use of social media as online learning technologies can be transferred into contexts of formal learning

Keywords: informal learning, postdigitality, songwriting, actor-network theory, community of musical practice, social media, TikTok, Instagram, apps

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Authors: Stéphanie Looser, Walter Wehrmeyer

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Given the limited research on Small and Mediumsized Enterprises’ (SMEs) contribution to Corporate Social Responsibility (CSR) and even scarcer research on Swiss SMEs, this paper helps to fill these gaps by enabling the identification of supranational SME parameters and to make a contribution to the evolving field of these topics. Thus, the paper investigates the current state of SME practices in Switzerland and across 15 other countries. Combining the degree to which SMEs demonstrate an explicit (or business case) approach or see CSR as an implicit moral activity with the assessment of their attributes for “variety of capitalism” defines the framework of this comparative analysis. According to previous studies, liberal market economies, e.g. in the United States (US) or United Kingdom (UK), are aligned with extrinsic CSR, while coordinated market systems (in Central European or Asian countries) evolve implicit CSR agendas. To outline Swiss small business CSR patterns in particular, 40 SME owner-managers were interviewed. The transcribed interviews were coded utilising MAXQDA for qualitative content analysis. A secondary data analysis of results from different countries (i.e., Australia, Austria, Chile, Cameroon, Catalonia (notably a part of Spain that seeks autonomy), China, Finland, Germany, Hong Kong (a special administrative region of China), Italy, Netherlands, Singapore, Spain, Taiwan, UK, US) lays groundwork for this comparative study on small business CSR. Applying the same coding categories (in MAXQDA) for the interview analysis as well as for the secondary data research while following grounded theory rules to refine and keep track of ideas generated testable hypotheses and comparative power on implicit (and the lower likelihood of explicit) CSR in SMEs retrospectively. The paper identifies Swiss small business CSR as deep, profound, “soul”, and an implicit part of the day-to-day business. Similar to most Central European, Mediterranean, Nordic, and Asian countries, explicit CSR is still very rare in Swiss SMEs. Astonishingly, also UK and US SMEs follow this pattern in spite of their strong and distinct liberal market economies. Though other findings show that nationality matters this research concludes that SME culture and its informal CSR agenda are strongly formative and superseding even forces of market economies, nationally cultural patterns, and language. In a world of “big business”, explicit “business case” CSR, and the mantra that “CSR must pay”, this study points to a distinctly implicit small business CSR model built on trust, physical closeness, and virtues that is largely detached from the bottom line. This pattern holds for different cultural contexts and it is concluded that SME culture is stronger than nationality leading to a supra-national, monolithic SME CSR approach. Hence, classifications of countries by their market system or capitalism, as found in the comparative capitalism literature, do not match the CSR practices in SMEs as they do not mirror the peculiarities of their business. This raises questions on the universality and generalisability of management concepts.

Keywords: CSR, comparative study, cultures of capitalism, small, medium-sized enterprises

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Authors: Chhordaneath Hen, Ty Sok, Ilan Ich, Ratboren Chan, Chantha Oeurng

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The damages caused by hydrological extremes such as flooding have been severe globally, and several research studies indicated extreme precipitations play a crucial role. Cambodia is one of the most vulnerable countries exposed to floods and drought as consequences of climate impact. Prek Thnot River Basin in the southwest part of Cambodia, which is in the plate and plateau region and a part of the Mekong Delta, was selected to investigate the changes in extreme precipitation and hydrological extreme. Furthermore, to develop a statistical relationship between these phenomena in this basin from 1995 to 2020 using Multiple Linear Regression. The precipitation and hydrological extreme were assessed via the attributes and trends of rainfall patterns during the study periods. The extreme flow was defined as a dependent variable, while the independent variables are various extreme precipitation indices. The study showed that all extreme precipitations indices (R10, R20, R35, CWD, R95p, R99p, and PRCPTOT) had increasing decency. However, the number of rain days per year had a decreasing tendency, which can conclude that extreme rainfall was more intense in a shorter period of the year. The study showed a similar relationship between extreme precipitation and hydrological extreme and land use change association with hydrological extreme. The direct combination of land use and precipitation equals 37% of the flood causes in this river. This study provided information on these two causes of flood events and an understanding of expectations of climate change consequences for flood and water resources management.

Keywords: extreme precipitation, hydrological extreme, land use, land cover, Prek Thnot river basin

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Authors: Nikk Leavitt, Peter Kellett, Cheryl Currie, Richard Larouche

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Background: Masculinity has been associated with poor mental health outcomes in adult men and is colloquially referred to as toxic. Masculinity is traditionally measured using the Male Role Norms Inventory, which examines behaviors that may be common in men but that are themselves associated with poor mental health regardless of gender (e.g., aggressiveness). The purpose of this study was to examine if masculinity is associated with generalized anxiety among men using this inventory vs. a man’s personal definition of it. Method: An online survey collected data from 1,200 men aged 18-65 across Canada in July 2022. Masculinity was measured using: 1) the Male Role Norms Inventory Short Form and 2) by asking men to self-define what being masculine means. Men were then asked to rate the extent they perceived themselves to be masculine on a scale of 1 to 10 based on their definition of the construct. Generalized anxiety disorder was measured using the GAD-7. Multiple linear regression was used to examine associations between each masculinity score and anxiety score, adjusting for confounders. Results: The masculinity score measured using the inventory was positively associated with increased anxiety scores among men (β = 0.02, p < 0.01). Masculinity subscales most strongly correlated with higher anxiety were restrictive emotionality (β = 0.29, p < 0.01) and dominance (β = 0.30, p < 0.01). When traditional masculinity was replaced by a man’s self-rated masculinity score in the model, the reverse association was found, with increasing masculinity resulting in a significantly reduced anxiety score (β = -0.13, p = 0.04). Discussion: These findings highlight the need to revisit the ways in which masculinity is defined and operationalized in research to better understand its impacts on men’s mental health. The findings also highlight the importance of allowing participants to self-define gender-based constructs, given they are fluid and socially constructed.

Keywords: masculinity, generalized anxiety disorder, race, intersectionality

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Authors: C. S. Wong, K. H. Badri, N. Ataollahi, K. P. Law, M. S. Su’ait, N. I. Hassan

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Novel bio-based polymer electrolyte was synthesized with LiClO4 as the main source of charge carrier. Initially, polyurethane-LiClO4 polymer electrolytes were synthesized via polymerization method with different NCO/OH ratios and labelled as PU1, PU2, PU3, and PU4. Subsequently, the chemical, thermal properties and ionic conductivity of the films produced were determined. Fourier transform infrared (FTIR) analysis indicates the co-ordination between Li+ ion and polyurethane in PU1 due to the greatest amount of hard segment of polyurethane in PU1 as proven by soxhlet analysis. The structures of polyurethanes were confirmed by 13 nuclear magnetic resonance spectroscopy (13C NMR) and FTIR spectroscopy. Differential scanning calorimetry (DSC) analysis indicates PU 1 has the highest glass transition temperature (Tg) corresponds to the most abundant urethane group which is the hard segment in PU1. Scanning electron microscopy (SEM) of the PU-LiClO4 shows the good miscibility between lithium salt and the polymer. The study found that PU1 possessed the greatest ionic conductivity (1.19 × 10-7 S.cm-1 at 298 K and 5.01 × 10-5 S.cm-1 at 373 K) and the lowest activation energy, Ea (0.32 eV) due to the greatest amount of hard segment formed in PU 1 induces the coordination between lithium ion and oxygen atom of carbonyl group in polyurethane. All the polyurethanes exhibited linear Arrhenius variations indicating ion transport via simple lithium ion hopping in polyurethane. This research proves the NCO content in polyurethane plays an important role in affecting the ionic conductivity of this polymer electrolyte.

Keywords: ionic conductivity, palm kernel oil-based monoester-OH, polyurethane, solid polymer electrolyte

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Authors: Joseph Sello Kau, Elias Mashayamombe, Brian Washington Madinkana, Cynthia Ngwane

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This paper presents the results for the statistical (stepwise linear regression and multiple regression) analyses, carried out on a number of crops in order to evaluate how the decision for crop choice affect the level of farm income generated by the farmers participating in the High Value Crop production (referred to as the HVC). The goal of the HVC is to encourage farmers cultivate fruit crops. The farmers received planting material from different extension agencies, together with other complementary packages such as fertilizer, garden tools, water tanks etc. During the surveys, it was discovered that a significant number of farmers were cultivating traditional crops even when their plot sizes were small. Traditional crops are competing for resources with high value crops. The results of the analyses show that farmers cultivating fruit crops, maize and potatoes were generating high income than those cultivating spinach and cabbage. High farm income is associated with plot size, access to social grants and gender. Choice for a crop is influenced by the availability of planting material and the market potential for the crop. Extension agencies providing the planting materials stand a good chance of having farmers follow their directives. As a recommendation, for the farmers to cultivate more of the HVCs, the ARC must intensify provision of fruit trees.

Keywords: farm income, nature of extension services, type of crops cultivated, fruit crops, cabbage, maize, potato and spinach

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Authors: Cherifi Katia, Al-Hawat Marie-Lynn, Tricou Leo-Paul, Lamontagne Stephanie, Tran Minh, Ngu Amy Ching Yie, Manrique Gabriela, Guirguis Natalie, Machuca Parra Arturo Israel, Matoori Simon

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Diabetic foot ulcers (DFUs) are a serious and prevalent complication of diabetes. Current diagnostic options are limited to macroscopic wound analysis such as wound size, depth, and infection. Molecular diagnostics promise to improve DFU diagnosis, staging, and assessment of treatment response. Here, we developed a rapid and easy-to-use fluorescent pH-sensing bandage for wound diagnostics. In a fluorescent dye screen, we identified pyranine as the lead compound due to its suitable pH-sensing properties in the clinically relevant pH range of 6 to 9. To minimize the release of this dye into the wound bed, we screened a library of ionic microparticles and found a strong adhesion of the anionic dye to a cationic polymeric microparticle. These dye-loaded microparticles showed a strong fluorescence response in the clinically relevant pH range of 6 to 9 and a dye release below 1% after one day in biological media. The dye-loaded microparticles were subsequently encapsulated in a calcium alginate hydrogel to minimize the interaction of the microparticles with the wound tissue. This pH-sensing diagnostic wound dressing was tested on full-thickness dorsal wounds of mice, and a linear fluorescence response (R2 = 0.9909) to clinically relevant pH values was observed. These findings encourage further development of this pH-sensing system for molecular diagnostics in DFUs.

Keywords: wound ph, fluorescence, diagnostics, diabetic foot ulcer, wound healing, chronic wounds, diabetes

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Authors: Khairul Zahreen Mohd Arof, Rahimah Muhamad

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Oil palm fiber (OPF) is a fibrous material produced from the waste of palm oil industry which is suitable to be used in construction industry. The applications of OPF in concrete can reduce the material costs and enhance concrete behavior. Dog-bone test provides significant results for investigating the behavior of fiber reinforced concrete under tensile loading. It is able to provide stress-strain profile, modulus of elasticity, stress at cracking point and total crack width. In this research, dog-bone tests have been conducted to analyze total crack width, stress-strain profile, and modulus of elasticity of OPFC. Specimens are in a dog-bone shape with a long notch in the middle as compared to the end, to ensure cracks occur only within the notch. Tests were instrumented using a universal testing machine Shimadzu 300kN, a linear variable differential transformer and two strain gauges. A total of nine specimens with different fibers at fiber volume fractions of 0.75%, 1.00%, and 1.25% have been tested to analyze the behavior under tensile loading. Also, three specimens of plain concrete fiber have been tested as control specimens. The tensile test of all specimens have been carried out for concrete age exceed 28 days. It shows that OPFC able to reduce total crack width. In addition, OPFC has higher cracking stress than plain concrete. The study shows plain concrete can be improved with the addition of OPF.

Keywords: cracks, crack width, dog-bone test, oil palm fiber concrete

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Authors: Andres Piña, Amy Meléndez, Pablo Cano, Tomas Cahuich

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The purpose of this project is to propose a quick and environmentally friendly alternative to measure the quality of oils used in food industry. There is evidence that repeated and indiscriminate use of oils in food processing cause physicochemical changes with formation of potentially toxic compounds that can affect the health of consumers and cause organoleptic changes. In order to assess the quality of oils, non-destructive optical techniques such as Interferometry offer a rapid alternative to the use of reagents, using only the interaction of light on the oil. Through this project, we used interferograms of samples of oil placed under different heating conditions to establish the changes in their quality. These interferograms were obtained by means of a Mach-Zehnder Interferometer using a beam of light from a HeNe laser of 10mW at 632.8nm. Each interferogram was captured, analyzed and measured full width at half-maximum (FWHM) using the software from Amcap and ImageJ. The total of FWHMs was organized in three groups. It was observed that the average obtained from each of the FWHMs of group A shows a behavior that is almost linear, therefore it is probable that the exposure time is not relevant when the oil is kept under constant temperature. Group B exhibits a slight exponential model when temperature raises between 373 K and 393 K. Results of the t-Student show a probability of 95% (0.05) of the existence of variation in the molecular composition of both samples. Furthermore, we found a correlation between the Iodine Indexes (Physicochemical Analysis) and the Interferograms (Optical Analysis) of group C. Based on these results, this project highlights the importance of the quality of the oils used in food industry and shows how Interferometry can be a useful tool for this purpose.

Keywords: food industry, interferometric, oils, quality control

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Authors: S. Nqayi, B. Sondezi

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A rare-earth-based Sm₂MnRuO₆₊δ (SMRO) double perovskite was prepared using a high-temperature solid-state reaction. The structural, morphological, chemical, thermodynamic, and magnetic properties were measured with X-ray diffraction (XRD), energy dispersive spectroscopy (EDS), X-ray photoemission spectroscopy (XPS), and vibrating sample magnetometer (VSM), respectively. The XRD revealed a tetragonal structure belonging to the I4/mmm space group, number 139, with linear Mn−O−Ru bonds. Replacing the well-studied alkaline earth metal with a rare-earth element increased the Mn-O bond length difference between the shorter equatorial (Mn-Oab) and the axial (Mn-Oc) bonds by approximately 6.3%. The elemental composition showed an O-rich double perovskite with a Ru deficit, which encourages the formation of a Ru⁶⁺ (d²) state. XPS spectra of Sm-3d, Ru-3d, and Mn-2p revealed the coexistence of a double oxidation state for each cation; Sm²⁺, Sm³⁺, Ru³⁺, Ru⁶⁺, Mn²⁺ , and Mn³⁺, in varying proportions. Entropy studies showed drastic ordering of spins at low temperatures (up to 12.4 K), whilst increasing temperatures above this point resulted in a drastic increase of disorder of the spins (up to 43.26 K), beyond which a constant slope of entropy is observed. Magnetic measurements revealed two magnetic ground states at TN = 12.4 K and TC = 43.3 K ordering antiferromagnetically (AFM) and ferromagnetically (FM), respectively. Kneller fit further showed that the materials become completely paramagnetic at TB = 88.1 K, (the blocking temperature). The existence of ferromagnetic (FM) super-exchange coupling in this work originating from Mn³⁺ (t³₂𝓰e¹𝓰)−O−Ru³⁺ (t⁵₂𝓰e⁰𝓰) and Mn²⁺ (t³₂𝓰e²𝓰−O−Ru⁶⁺ (t²₂𝓰e⁰𝓰) which plays an important role in suppressing the Mn/Ru−O−Mn/Ru antiferromagnetic (AFM) interactions.

Keywords: solid-state reaction, super-exchange coupling, ferromagnetic, Kneller’s law, entropy

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Authors: Jocelyne Uwibambe, Ange Thaina Ndizeye, Dinah Ishimwe, Emmanuel Mugabo Byakagaba

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Background: In low- and middle-income countries (LMICs), the use of modern contraceptive methods among women, including adolescents, is still low despite the desire to avoid pregnancy. In addition, countries have set a minimum age for marriage, which is 21 years for most countries, including Rwanda. The Rwandan culture, to a certain extent, and religion, to a greater extent, however, limit the freedom of young women to use contraceptive services because it is wrongly perceived as an encouragement for premarital sexual intercourse. In the end, what doesn’t change is that denying access to contraceptives to either male or female adolescents does not translate into preventing them from sexual activities, hence leading to an ever-increasing number of unwanted pregnancies, possible STIs, HIV, Human Papilloma Virus, and subsequent unsafe abortion followed by avoidable expensive complications. The purpose of this study is to evaluate the perception of healthcare providers regarding contraceptive use among adolescents. Methodology: This was a qualitative study. Interviews were done with different healthcare providers, including doctors, nurses, midwives, and pharmacists, through focused group discussions and in-depth interviews, then the audio was transcribed, translated and thematic coding was done. Results: This study explored the perceptions of healthcare workers regarding the provision of modern contraception to adolescents in Rwanda. The findings revealed that while healthcare providers had a good understanding of family planning and contraception, they were hesitant to provide contraception to adolescents. Sociocultural beliefs played a significant role in shaping their attitudes, as many healthcare workers believed that providing contraception to adolescents would encourage promiscuous behavior and go against cultural norms. Religious beliefs also influenced their reluctance, with some healthcare providers considering premarital sex and contraception as sinful. Lack of knowledge among parents and adolescents themselves was identified as a contributing factor to unwanted pregnancies, as inaccurate information from peers and social media influenced risky sexual behavior. Conditional policies, such as the requirement for parental consent, further hindered adolescents' access to contraception. The study suggested several solutions, including comprehensive sexual and reproductive health education, involving multiple stakeholders, ensuring easy access to contraception, and involving adolescents in policymaking. Overall, this research highlights the need for addressing sociocultural beliefs, improving healthcare providers' knowledge, and revisiting policies to ensure adolescents' reproductive health rights are met in Rwanda. Conclusion: The study highlights the importance of enhancing healthcare provider training, expanding access to modern contraception, implementing community-based interventions, and strengthening policy and programmatic support for adolescent contraception. Addressing these challenges is crucial for improving the provision of family planning services to adolescents in Rwanda and achieving the Sustainable Development Goals related to sexual and reproductive health. Collaborative efforts involving various stakeholders and organizations can contribute to overcoming these barriers and promoting the well-being of adolescents in Rwanda.

Keywords: adolescent, health care providers, contraception, reproductive health

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Authors: Sagar Pandit

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The present work focuses on the effect of various process parameters on the mechanical properties and microstructure of joints produced by continuous drive friction welding and linear friction welding. An attempt is made to investigate the feasibility of obtaining an acceptable weld joint between similar as well as dissimilar components and the microstructural changes have also been assessed once the good weld joints were considered (using Optical Microscopy and Scanning Electron Microscopy techniques). The impact of forging pressure in the microstructure of the weld joint has been studied and the variation in joint strength with varying forge pressure is analyzed. The weld joints were obtained two pair of dissimilar materials and one pair of similar materials, which are listed respectively as: Al-AA5083 & Cu-C101 (dissimilar), Aluminium alloy-3000 series & Mild Steel (dissimilar) and High Nitrogen Austenitic Stainless Steel pair (similar). Intermetallic phase formation was observed at the weld joints in the Al-Cu joint, which consequently harmed the properties of the joint (less tensile strength). It was also concluded that the increase in forging pressure led to both increment and decrement in the tensile strength of the joint depending on the similarity or dissimilarity of the components. The hardness was also observed to possess maximum as well as minimum values at the weld joint depending on the similarity or dissimilarity of workpieces. It was also suggested that a higher forging pressure is needed to obtain complete joining for the formation of the weld joint.

Keywords: forging pressure, friction welding, mechanical properties, microstructure

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Authors: Adel. M. Sharaf, Foad H. Gandoman

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Nowadays, Photovoltaic-PV Farms/ Parks and large PV-Smart Grid Interface Schemes are emerging and commonly utilized in Renewable Energy distributed generation. However, PV-hybrid-Dc-Ac Schemes using interface power electronic converters usually has negative impact on power quality and stabilization of modern electrical network under load excursions and network fault conditions in smart grid. Consequently, robust FACTS based interface schemes are required to ensure efficient energy utilization and stabilization of bus voltages as well as limiting switching/fault onrush current condition. FACTS devices are also used in smart grid-Battery Interface and Storage Schemes with PV-Battery Storage hybrid systems as an elegant alternative to renewable energy utilization with backup battery storage for electric utility energy and demand side management to provide needed energy and power capacity under heavy load conditions. The paper presents a robust interface PV-Li-Ion Battery Storage Interface Scheme for Distribution/Utilization Low Voltage Interface using FACTS stabilization enhancement and dynamic maximum PV power tracking controllers. Digital simulation and validation of the proposed scheme is done using MATLAB/Simulink software environment for Low Voltage- Distribution/Utilization system feeding a hybrid Linear-Motorized inrush and nonlinear type loads from a DC-AC Interface VSC-6-pulse Inverter Fed from the PV Park/Farm with a back-up Li-Ion Storage Battery.

Keywords: AC FACTS, smart grid, stabilization, PV-battery storage, Switched Filter-Compensation (SFC)

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Authors: Dileep Kumar Gupta, Rajendra Prasad, Pradeep Kumar, Varun Narayan Mishra, Ajeet Kumar Vishwakarma, Prashant K. Srivastava

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An approach was evaluated for the retrieval of soil moisture of bare soil surface using bistatic scatterometer data in the angular range of 200 to 700 at VV- and HH- polarization. The microwave data was acquired by specially designed X-band (10 GHz) bistatic scatterometer. The linear regression analysis was done between scattering coefficients and soil moisture content to select the suitable incidence angle for retrieval of soil moisture content. The 250 incidence angle was found more suitable. The support vector regression analysis was used to approximate the function described by the input-output relationship between the scattering coefficient and corresponding measured values of the soil moisture content. The performance of support vector regression algorithm was evaluated by comparing the observed and the estimated soil moisture content by statistical performance indices %Bias, root mean squared error (RMSE) and Nash-Sutcliffe Efficiency (NSE). The values of %Bias, root mean squared error (RMSE) and Nash-Sutcliffe Efficiency (NSE) were found 2.9451, 1.0986, and 0.9214, respectively at HH-polarization. At VV- polarization, the values of %Bias, root mean squared error (RMSE) and Nash-Sutcliffe Efficiency (NSE) were found 3.6186, 0.9373, and 0.9428, respectively.

Keywords: bistatic scatterometer, soil moisture, support vector regression, RMSE, %Bias, NSE

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