Search results for: lead-time estimation

Authors: Yu-Cheng Chen, Tzu-Ping Lin, Feng-Yi Lin, Chih-Yu Chen

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Due to the urban heat island effect caused by highly development of city, the heat stress increased in recent year rapidly. Resulting in a sharp raise of the energy used in urban area. The heat stress during summer time exacerbated the usage of air conditioning and electric equipment, which caused more energy consumption and anthropogenic heat. Therefore, an accurate and simple method to measure energy used in urban area can be helpful for the architectures and urban planners to develop better energy efficiency goals. This research applies the combination of airborne LiDAR data and thermal imager to provide an innovate method to estimate energy consumption. Owing to the high resolution of remote sensing data, the accurate current volume and total floor area and the surface temperature of building derived from LiDAR and thermal imager can be herein obtained to predict energy used. In the estimate process, the LiDAR data will be divided into four type of land cover which including building, road, vegetation, and other obstacles. In this study, the points belong to building were selected to overlay with the land use information; therefore, the energy consumption can be estimated precisely with the real value of total floor area and energy use index for different use of building. After validating with the real energy used data from the government, the result shows the higher building in high development area like commercial district will present in higher energy consumption, caused by the large quantity of total floor area and more anthropogenic heat. Furthermore, because of the surface temperature can be warm up by electric equipment used, this study also applies the thermal image of building to find the hot spots of energy used and make the estimation method more complete.

Keywords: urban heat island, urban planning, LiDAR, thermal imager, energy consumption

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Authors: Ehsan Mehryaar, Seyed Armin Motahari Tabari

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Coarse-grained soils are known and commonly used in a wide range of geotechnical projects, including high earth dams or embankments for their high shear strength. The most important engineering property of these soils is friction angle which represents the interlocking between soil particles and can be applied widely in designing and constructing these earth structures. Friction angle and dilative behavior of coarse-grained soils can be estimated from empirical correlations with in-situ testing and physical properties of the soil or measured directly in the laboratory performing direct shear or triaxial tests. Unfortunately, large-scale testing is difficult, challenging, and expensive and is not possible in most soil mechanic laboratories. So, it is common to remove the large particles and do the tests, which cannot be counted as an exact estimation of the parameters and behavior of the original soil. This paper describes a new methodology to simulate particles grading distribution of a well-graded gravel sample to a smaller scale sample as it can be tested in an ordinary direct shear apparatus to estimate the stress-strain behavior, friction angle, and dilative behavior of the original coarse-grained soil considering its confining pressure, and relative density using a machine learning method. A total number of 72 direct shear tests are performed in 6 different sizes, 3 different confining pressures, and 4 different relative densities. Multivariate Adaptive Regression Spline (MARS) technique was used to develop an equation in order to predict shear strength and dilative behavior based on the size distribution of coarse-grained soil particles. Also, an uncertainty analysis was performed in order to examine the reliability of the proposed equation.

Keywords: MARS, coarse-grained soil, shear strength, uncertainty analysis

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Authors: Naamane Remita, Mohammed laïd Mechri, Nouredine Zekri, Smaïl Chihi

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The goal of this study is the estimation real and imaginary components of both electrical impedance and permittivity z', z'' and ε', ε'' respectively, in Ouargla dunes sand at different temperatures and different frequencies, with alternating current (AC) equal to 1 volt, using the impedance spectroscopy (IS). This method is simple and non-destructive. the results can frequently be correlated with a number of physical properties, dielectric properties and the impacts of the composition on the electrical conductivity of solids. The experimental results revealed that the real part of impedance is higher at higher temperature in the lower frequency region and gradually decreases with increasing frequency. As for the high frequencies, all the values of the real part of the impedance were positive. But at low frequency the values of the imaginary part were positive at all temperatures except for 1200 degrees which were negative. As for the medium frequencies, the reactance values were negative at temperatures 25, 400, 200 and 600 degrees, and then became positive at the rest of the temperatures. At high frequencies of the order of MHz, the values of the imaginary part of the electrical impedance were in contrast to what we recorded for the middle frequencies. The results showed that the electrical permittivity decreases with increasing frequency, at low frequency we recorded permittivity values of 10+ 11, and at medium frequencies it was 10+ 07, while at high frequencies it was 10+ 02. The values of the real part of the electrical permittivity were taken large values at the temperatures of 200 and 600 degrees Celsius and at the lowest frequency, while the smallest value for the permittivity was recorded at the temperature of 400 degrees Celsius at the highest frequency. The results showed that there are large values of the imaginary part of the electrical permittivity at the lowest frequency and then it starts decreasing as the latter increases (the higher the frequency the lower the values of the imaginary part of the electrical permittivity). The character of electrical impedance variation indicated an opportunity to realize the polarization of Ouargla dunes sand and acquaintance if this compound consumes or produces energy. It’s also possible to know the satisfactory of equivalent electric circuit, whether it’s miles induction or capacitance.

Keywords: electrical impedance, electrical permittivity, temperature, impedance spectroscopy, dunes sand ouargla

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Authors: Saoussen Dhib, Chris M. Mannaerts, Zoubeida Bargaoui, Ben H. P. Maathuis, Petra Budde

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In this study, a new method to delineate rain areas in northern Tunisia is presented. The proposed approach is based on the blending of the geostationary Meteosat Second Generation (MSG) infrared channel (IR) with the low-earth orbiting passive Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI). To blend this two products, we need to apply two main steps. Firstly, we have to identify the rainy pixels. This step is achieved based on a classification using MSG channel IR 10.8 and the water vapor WV 0.62, applying a threshold on the temperature difference of less than 11 Kelvin which is an approximation of the clouds that have a high likelihood of precipitation. The second step consists on fitting the relation between IR cloud top temperature with the TMI rain rates. The correlation coefficient of these two variables has a negative tendency, meaning that with decreasing temperature there is an increase in rainfall intensity. The fitting equation will be applied for the whole day of MSG 15 minutes interval images which will be summed. To validate this combined product, daily extreme rainfall events occurred during the period 2007-2009 were selected, using a threshold criterion for large rainfall depth (> 50 mm/day) occurring at least at one rainfall station. Inverse distance interpolation method was applied to generate rainfall maps for the drier summer season (from May to October) and the wet winter season (from November to April). The evaluation results of the estimated rainfall combining MSG and TMI was very encouraging where all the events were detected rainy and the correlation coefficients were much better than previous evaluated products over the study area such as MSGMPE and PERSIANN products. The combined product showed a better performance during wet season. We notice also an overestimation of the maximal estimated rain for many events.

Keywords: combination, extreme, rainfall, TMI-MSG, Tunisia

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Authors: Xiaoqin Wang, Li Yin

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Background and purpose: In many practices, one estimates causal effects arising from a complex stochastic process, where a sequence of treatments are assigned to influence a certain outcome of interest, and there exist time-dependent covariates between treatments. When covariates are plentiful and/or continuous, statistical modeling is needed to reduce the huge dimensionality of the problem and allow for the estimation of causal effects. Recently, Wang and Yin (Annals of statistics, 2020) derived a new general formula, which expresses these causal effects in terms of the point effects of treatments in single-point causal inference. As a result, it is possible to conduct the modeling via point effects. The purpose of the work is to study the modeling of these causal effects via point effects. Challenges and solutions: The time-dependent covariates often have influences from earlier treatments as well as on subsequent treatments. Consequently, the standard parameters – i.e., the mean of the outcome given all treatments and covariates-- are essentially all different (null paradox). Furthermore, the dimension of the parameters is huge (curse of dimensionality). Therefore, it can be difficult to conduct the modeling in terms of standard parameters. Instead of standard parameters, we have use point effects of treatments to develop likelihood-based parametric approach to the modeling of these causal effects and are able to model the causal effects of a sequence of treatments by modeling a small number of point effects of individual treatment Achievements: We are able to conduct the modeling of the causal effects from a sequence of treatments in the familiar framework of single-point causal inference. The simulation shows that our method achieves not only an unbiased estimate for the causal effect but also the nominal level of type I error and a low level of type II error for the hypothesis testing. We have applied this method to a longitudinal study of COVID-19 mortality among Scandinavian countries and found that the Swedish approach performed far worse than the other countries' approach for COVID-19 mortality and the poor performance was largely due to its early measure during the initial period of the pandemic.

Keywords: causal effect, point effect, statistical modelling, sequential causal inference

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Authors: Hung-Sheng Lin, Cheng-Hsuan Li

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Over the past few years, kernel-based algorithms have been widely used to extend some linear feature extraction methods such as principal component analysis (PCA), linear discriminate analysis (LDA), and nonparametric weighted feature extraction (NWFE) to their nonlinear versions, kernel principal component analysis (KPCA), generalized discriminate analysis (GDA), and kernel nonparametric weighted feature extraction (KNWFE), respectively. These nonlinear feature extraction methods can detect nonlinear directions with the largest nonlinear variance or the largest class separability based on the given kernel function. Moreover, they have been applied to improve the target detection or the image classification of hyperspectral images. The double nearest proportion feature extraction (DNP) can effectively reduce the overlap effect and have good performance in hyperspectral image classification. The DNP structure is an extension of the k-nearest neighbor technique. For each sample, there are two corresponding nearest proportions of samples, the self-class nearest proportion and the other-class nearest proportion. The term “nearest proportion” used here consider both the local information and other more global information. With these settings, the effect of the overlap between the sample distributions can be reduced. Usually, the maximum likelihood estimator and the related unbiased estimator are not ideal estimators in high dimensional inference problems, particularly in small data-size situation. Hence, an improved estimator by shrinkage estimation (regularization) is proposed. Based on the DNP structure, LDA is included as a special case. In this paper, the kernel method is applied to extend DNP to kernel-based DNP (KDNP). In addition to the advantages of DNP, KDNP surpasses DNP in the experimental results. According to the experiments on the real hyperspectral image data sets, the classification performance of KDNP is better than that of PCA, LDA, NWFE, and their kernel versions, KPCA, GDA, and KNWFE.

Keywords: feature extraction, kernel method, double nearest proportion feature extraction, kernel double nearest feature extraction

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Authors: Oyatayo Kehinde Taofik, Ndabula Christopher

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Drainage basins are a central theme in the green economy. The rising challenges in flooding, erosion or sediment transport and sedimentation threaten the green economy. This has led to increasing emphasis on quantitative analysis of drainage basin parameters for better understanding, estimation and prediction of fluvial responses and, thus associated hazards or disasters. This can be achieved through direct measurement, characterization, parameterization, or modeling. This study applied the Remote Sensing and Geographic Information System approach of parameterization and characterization of the morphometric variables of Katsina – Ala basin using a 30 m resolution Shuttle Radar Topographic Mission (SRTM) Digital Elevation Model (DEM). This was complemented with topographic and hydrological maps of Katsina-Ala on a scale of 1:50,000. Linear, areal and relief parameters were characterized. The result of the study shows that Ala and Udene sub-watersheds are 4th and 5th order basins, respectively. The stream network shows a dendritic pattern, indicating homogeneity in texture and a lack of structural control in the study area. Ala and Udene sub-watersheds have the following values for elongation ratio, circularity ratio, form factor and relief ratio: 0.48 / 0.39 / 0.35/ 9.97 and 0.40 / 0.35 / 0.32 / 6.0. They also have the following values for drainage texture and ruggedness index of 0.86 / 0.011 and 1.57 / 0.016. The study concludes that the two sub-watersheds are elongated, suggesting that they are susceptible to erosion and, thus higher sediment load in the river channels, which will dispose the watersheds to higher flood peaks. The study also concludes that the sub-watersheds have a very coarse texture, with good permeability of subsurface materials and infiltration capacity, which significantly recharge the groundwater. The study recommends that efforts should be put in place by the Local and State Governments to reduce the size of paved surfaces in these sub-watersheds by implementing a robust agroforestry program at the grass root level.

Keywords: erosion, flood, mitigation, morphometry, watershed

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Authors: Sehrish Qayyum

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In this age of constant technology, asymmetrical warfare could not be won. Attuned psychometric study confirms that screaming sometimes is more productive than active retaliation against strong adversaries. Asymmetric warfare is a game of nerves and thoughts with least vigorous participation for large anticipated losses. It creates the condition of paraplegia with partial but permanent immobility, which effects the core warfare operations, being screams rather than active retaliation. When one’s own power is doubted, it gives power to one’s own doubt to ruin all planning either done with superlative cost-benefit analysis. Strategically calculated estimation of asymmetric warfare since the early WWI to WWII, WWII-to Cold War, and then to the current era in three chronological periods exposits that courage makes nations win the battle of warriors to battle of comrades. Asymmetric warfare has been most difficult to fight and survive due to unexpectedness and being lethal despite preparations. Thoughts before action may be the best-assumed strategy to mix Regional Security Complex Theory and OODA loop to develop the Paraplegic Resilience Policy Plan (PRPP) to win asymmetric warfare. PRPP may serve to control and halt the ongoing wave of terrorism, guerilla warfare, and insurgencies, etc. PRPP, along with a strategic work plan, is based on psychometric analysis to deal with any possible war condition and tactic to save millions of innocent lives such that lost in Christchurch New Zealand in 2019, November 2015 Paris attacks, and Berlin market attacks in 2016, etc. Getting tangled into self-imposed epistemic dilemmas results in regret that becomes the only option of performance. It is a descriptive psychometric analysis of war conditions with generic application of probability tests to find the best possible options and conditions to develop PRPP for any adverse condition possible so far. Innovation in technology begets innovation in planning and action-plan to serve as a rheostat approach to deal with asymmetric warfare.

Keywords: asymmetric warfare, psychometric analysis, PRPP, security

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Authors: S. P. Singh, Priya, Komal Sajwan

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With the increasing spread of organic farming in India, costs, returns, efficiency, and social and environmental sustainability of organic vis-a-vis conventional farming systems have become topics of interest among agriculture scientists, economists, and policy analysts. A study on technical efficiency estimation under these farming systems, particularly in the Ganga River Basin, where the promotion of organic farming is incentivized, can help to understand whether the inputs are utilized to their maximum possible level and what measures can be taken to improve the efficiency. This paper, therefore, analyses the technical efficiency of wheat farms operating under organic and conventional farming systems. The study is based on a primary survey of 600 farms (300 organic ad 300 conventional) conducted in 2021 in two districts located in the Middle Ganga River Basin, India. Technical, managerial, and scale efficiencies of individual farms are estimated by applying the data envelopment analysis (DEA) methodology. The per hectare value of wheat production is taken as an output variable, and values of seeds, human labour, machine cost, plant nutrients, farm yard manure (FYM), plant protection, and irrigation charges are considered input variables for estimating the farm-level efficiencies. The post-DEA analysis is conducted using the Tobit regression model to know the efficiency determining factors. The results show that technical efficiency is significantly higher in conventional than organic farming systems due to a higher gap in scale efficiency than managerial efficiency. Further, 9.8% conventional and only 1.0% organic farms are found operating at the most productive scale size (MPSS), and 99% organic and 81% conventional farms at IRS. Organic farms perform well in managerial efficiency, but their technical efficiency is lower than conventional farms, mainly due to their relatively lower scale size. The paper suggests that technical efficiency in organic wheat can be increased by upscaling the farm size by incentivizing group/collective farming in clusters.

Keywords: organic, conventional, technical efficiency, determinants, DEA, Tobit regression

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Authors: Kefi Rahmadio, Filipus Armando Ginting, Richard Nainggolan

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This research shows the formation of the West Buru Basin and the potential utilization of this West Buru Basin as a geothermal potential. The research area is West Buru Island which is part of the West Buru Basin. The island is located in Maluku Province, with its capital city named Namlea. The island is divided into 10 districts, namely District Kepalamadan, Airbuaya District, Wapelau District, Namlea District, Waeapo District, Batabual District, Namrole District, Waesama District, Leksula District, and Ambalau District. The formation in this basin is Permian-Quarter. They start from the Formation Ghegan, Dalan Formation, Mefa Formation, Kuma Formation, Waeken Formation, Wakatin Formation, Ftau Formation and Leko Formation. These formations are composing this West Buru Basin. Determination of prospect area in the geothermal area with preliminary investigation stage through observation of manifestation, topographic shape and structure are found around prospect area. This is done because there is no data of earth that support the determination of prospect area more accurately. In Waepo area, electric power generated based on field observation and structural analysis, geothermal area of ​Waeapo was approximately 6 km², with reference to the SNI 'Classification of Geothermal Potential' (No.03-5012-1999), an area of ​​1 km² is assumed to be 12.5 MWe. The speculative potential of this area is (Q) = 6 x 12.5 MWe = 75 MWe. In the Bata Bual area, the geothermal prospect projected 4 km², the speculative potential of the Bata Bual area is worth (Q) = 4 x 12.5 MWe = 50 MWe. In Kepala Madan area, based on the estimation of manifestation area, there is a wide area of ​​prospect in Kepala Madan area about 4 km². The geothermal energy potential of the speculative level in Kepala Madan district is (Q) = 4 x 12.5 MWe = 50 MWe. These three areas are the largest geothermal potential on the island of West Buru. From the above research, it can be concluded that there is potential in West Buru Island. Further exploration is needed to find greater potential. Therefore, researchers want to explain the geothermal potential contained in the West Buru Basin, within the scope of West Buru Island. This potential can be utilized for the community of West Buru Island.

Keywords: West Buru basin, West Buru island, potential, Waepo, Bata Bual, Kepala Madan

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Authors: Waqas Ahmed Khan Afridi, Subhas Chandra Mukhopadhyay, Bandita Mainali

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The research paper presents a unique approach to evapotranspiration (ET) prediction using a Support Vector Machine (SVM) learning algorithm. The study leverages real-time sensor node data to develop an accurate and adaptable prediction model, addressing the inherent challenges of traditional ET estimation methods. The integration of the SVM algorithm with real-time sensor node data offers great potential to improve spatial and temporal resolution in ET predictions. In the model development, key input features are measured and computed using mathematical equations such as Penman-Monteith (FAO56) and soil water balance (SWB), which include soil-environmental parameters such as; solar radiation (Rs), air temperature (T), atmospheric pressure (P), relative humidity (RH), wind speed (u2), rain (R), deep percolation (DP), soil temperature (ST), and change in soil moisture (∆SM). The one-year field data are split into combinations of three proportions i.e. train, test, and validation sets. While kernel functions with tuning hyperparameters have been used to train and improve the accuracy of the prediction model with multiple iterations. This paper also outlines the existing methods and the machine learning techniques to determine Evapotranspiration, data collection and preprocessing, model construction, and evaluation metrics, highlighting the significance of SVM in advancing the field of ET prediction. The results demonstrate the robustness and high predictability of the developed model on the basis of performance evaluation metrics (R2, RMSE, MAE). The effectiveness of the proposed model in capturing complex relationships within soil and environmental parameters provide insights into its potential applications for water resource management and hydrological ecosystem.

Keywords: evapotranspiration, FAO56, KNIME, machine learning, RStudio, SVM, sensors

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Authors: Sung-Jun Yoo, Kazuhide Ito

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In this study, a comprehensive computer simulated person (CSP) that integrates computational human model (virtual manikin) and respiratory tract model (virtual airway), was applied for estimation of indoor environmental quality. Moreover, an inclusive prediction method was established by integrating computational fluid dynamics (CFD) analysis with advanced CSP which is combined with physiologically-based pharmacokinetic (PBPK) model, unsteady thermoregulation model for analysis targeting micro-climate around human body and respiratory area with high accuracy. This comprehensive method can estimate not only the contaminant inhalation but also constant interaction in the contaminant transfer between indoor spaces, i.e., a target area for indoor air quality (IAQ) assessment, and respiratory zone for health risk assessment. This study focused on the usage of the CSP as an air/thermal quality sensor in indoors, which means the application of comprehensive model for assessment of IAQ and thermal environmental quality. Demonstrative analysis was performed in order to examine the applicability of the comprehensive model to the heating, ventilation, air conditioning (HVAC) control scheme. CSP was located at the center of the simple model room which has dimension of 3m×3m×3m. Formaldehyde which is generated from floor material was assumed as a target contaminant, and flow field, sensible/latent heat and contaminant transfer analysis in indoor space were conducted by using CFD simulation coupled with CSP. In this analysis, thermal comfort was evaluated by thermoregulatory analysis, and respiratory exposure risks represented by adsorption flux/concentration at airway wall surface were estimated by PBPK-CFD hybrid analysis. These Analysis results concerning IAQ and thermal comfort will be fed back to the HVAC control and could be used to find a suitable ventilation rate and energy requirement for air conditioning system.

Keywords: CFD simulation, computer simulated person, HVAC control, indoor environmental quality

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Authors: Rashmi Tomar, A. Srinivasan, Nayan K. Mohanty, Arun K. Jain

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Phthalates have been used as additives in industrial products since the 1930s, and are universally considered to be ubiquitous environmental contaminants. The general population is exposed to phthalates through consumer products, as well as diet and medical treatments. Animal studies showing the existence of an association between some phthalates and testicular toxicity have generated public and scientific concern about the potential adverse effects of environmental changes on male reproductive health. Unprecedented declines in fertility rates and semen quality have been reported during the last half of the 20th century in developed countries and increasing interest exists on the potential relationship between exposure to environmental contaminants, including phthalates, and human male reproductive health Studies. Phthalates may be associated with altered endocrine function and adverse effects on male reproductive development and function, but human studies are limited. The aim of the present study was detection of phthalate compounds, estimation of their metabolites in infertile & fertile male. Blood and urine samples were collected from 150 infertile patients & 75 fertile volunteers recruited through Department of Urology, Safdarjung Hospital, New Delhi. Blood have been collected in separate glass tubes from the antecubital vein of the patients, serum have been separate and estimate the phthalate level in serum samples by Gas Chromatography / Mass Spectrometry using NIOSH / OSHA detailed protocol. Urine of Infertile & Fertile Subjects was collected & extracted using solid phase extraction method, analysis by HPLC. In conclusion, to the best of our knowledge the present study based on human is first to show the presence of phthalate in human serum samples and their metabolites in urine samples. Significant differences were observed between several phthalates in infertile and fertile healthy individuals.

Keywords: Gas Chromatography, HPLC, male infertility, phthalates, serum, toxicity, urine

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Authors: Veerpal Kaur

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The present paper aims at investigating the role of dynamic externalities of agglomeration in the regional productivity growth of manufacturing sector in India and China. Taking 2-digit level manufacturing sector data of states and provinces of India and China respectively for the period of 1998-99 to 2011-12, this paper examines the effect of dynamic externalities namely – Marshall-Arrow-Romer (MAR) specialization externalities, Jacobs’s diversity externalities, and Porter’s competition externalities on regional total factor productivity growth (TFPG) of manufacturing sector in both economies. Regressions have been carried on pooled data for all 2-digit manufacturing industries for India and China separately. The estimation of Panel has been based on a fixed effect by sector model. The results of econometric exercise show that labour-intensive industries in Indian regional manufacturing benefit from diversity externalities and capital intensive industries gain more from specialization in terms of TFPG. In China, diversity externalities and competition externalities hold better prospectus for regional TFPG in both labour intensive and capital intensive industries. But if we look at results for coastal and non-coastal region separately, specialization tends to assert a positive effect on TFPG in coastal regions whereas it has a negative effect on TFPG of coastal regions. Competition externalities put a negative effect on TFPG of non-coastal regions whereas it has a positive effect on TFPG of coastal regions. Diversity externalities made a positive contribution to TFPG in both coastal and non-coastal regions. So the results of the study postulate that the importance of dynamic externalities should not be examined by pooling all industries and all regions together. This could hold differential implications for region specific and industry-specific policy formulation. Other important variables explaining regional level TFPG in both India and China have been the availability of infrastructure, level of competitiveness, foreign direct investment, exports and geographical location of the region (especially in China).

Keywords: China, dynamic externalities, India, manufacturing, productivity

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Authors: Emejeamara Francis

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The future of potentials wind energy applications within suburban/urban areas are currently faced with various problems. These include insufficient assessment of urban wind resource, and the effectiveness of commercial gust control solutions as well as unavailability of effective and cheaper valuable tools for scoping the potentials of urban wind applications within built-up environments. In order to achieve effective assessment of the potentials of urban wind installations, an estimation of the total energy that would be available to them were effective control systems to be used, and evaluating the potential power to be generated by the wind system is required. This paper presents a methodology of predicting the power generated by a wind system operating within an urban wind resource. This method was developed by using high temporal resolution wind measurements from eight potential sites within the urban and suburban environment as inputs to a vertical axis wind turbine multiple stream tube model. A relationship between the unsteady performance coefficient obtained from the stream tube model results and turbulence intensity was demonstrated. Hence, an analytical methodology for estimating the unsteady power coefficient at a potential turbine site is proposed. This is combined with analytical models that were developed to predict the wind speed and the excess energy (EEC) available in estimating the potential power generated by wind systems at different heights within a built environment. Estimates of turbulence intensities, wind speed, EEC and turbine performance based on the current methodology allow a more complete assessment of available wind resource and potential urban wind projects. This methodology is applied to four major UK cities namely Leeds, Manchester, London and Edinburgh and the potential to map the turbine performance at different heights within a typical urban city is demonstrated.

Keywords: small-scale wind, turbine power, urban wind energy, turbulence intensity, excess energy content

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Authors: Ezat Vahidian, Babak Eshrati

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Introduction: Elder abuse, a very complex issue with diverse definitions and names, has been very slow to capture the public eye and public policy since it is manifested at many levels. It requires the involvement of different types of professionals. While elder abuse is not a new phenomenon, the speed of population ageing world-wide is likely to lead to an increase in its incidence and prevalence. Elder abuse has devastating consequences for older persons such as poor quality of life, psychological distress, and loss of property and security. It is also associated with increased mortality and morbidity. Elder abuse is a problem that manifests itself in both rich and poor countries and at all levels of society. Purpose: The purpose of this study is to determine the prevalence of elder abuse and effects of social factor on it in Markazi Province. Materials and methods: The society of the study was all of the elders in Markazi Province that were available by geographical address in the table of rural and urban household societies. The study was cross sectional and multi phases in sampling the first one was classification according rural and urban area and the second one was cluster sampling with equal cluster. Estimation of samples were 472 persons and increased by design effect to 1110 persons. Collection data was done by questionnaire and analyzed by SPSS and chi 2 exam. Results: This study showed 70 persons were abused (42/8% male and 57/2% female) mean of ages was 74/7 years. 64% were marred and 31% were widows. There were not any significant meaningful association between elder abuse and area of living (pv=0.299),occupation (p.v=0.104), education (pv=0.358) and age (P.value=0.104) there were significant meaningful association between physical impairment (pv=0.08), and movement impairment (P.value=0.008). Conclusion: Results verify that maltreatment occurred in the aged persons. Analysis of data indicated that elder abuse exist in every socioeconomic group with any context of education in urban area and rural area and in men and women. Prevalence of elder abuse was 6.3% (70 persons) that verify the data of developed countries with limited sample.

Keywords: elder abuse, education, occupation, area of living

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Authors: Gargi Prasad, Ashiho A. Mao, Deepu Vijayan, S. Mandal

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The main objective of the present study was to optimize and develop an efficient protocol for in vitro propagation of a medicinally important orchid Cephalantheropsis obcordata (Lindl.) Ormerod along with genetic stability analysis of regenerated plants. This plant has been traditionally used in Chinese folk medicine and the decoction of whole plant is known to possess anticancer activity. Nodal segments used as explants were inoculated on Murashige and Skoog (MS) medium supplemented with various concentrations of isopentenyl adenine (2iP). The rooted plants were successfully acclimatized in the greenhouse with 100% survival rate. Inter-simple sequence repeats (ISSR) markers were used to assess the genetic fidelity of in vitro raised plants and the mother plant. It was revealed that monomorphic bands showing the absence of polymorphism in all in vitro raised plantlets analyzed, confirming the genetic uniformity among the regenerants. Phytochemical analysis was done to compare the antioxidant activities and HPLC fingerprinting assay of 80% aqueous ethanol extract of the leaves and stem of in vitro and in vivo grown C. obcordata. The extracts of the plants were examined for their antioxidant activities by using free radical 1, 1-diphenyl-2-picryl hydrazyl (DPPH) scavenging method, 2,2’-azino-bis (3-ethylbenzothiazoline-6-sulfonic acid) (ABTS) radical scavenging ability, reducing power capacity, estimation of total phenolic content, flavonoid content and flavonol content. A simplified method for the detection of ascorbic acid, phenolic acids and flavonoids content was also developed by using reversed phase high-performance liquid chromatography (HPLC). This is the first report on the micropropagation, genetic integrity study and quantitative phytochemical analysis of in vitro regenerated plants of C. obcordata.

Keywords: Cephalantheropsis obcordata, genetic fidelity, ISSR markers, HPLC

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Authors: Dimitriya A. Mihaylova, Zlatka V. Valkova-Jarvis, Georgi L. Iliev

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One possible approach for maintaining the security of communication systems relies on Physical Layer Security mechanisms. However, in wireless time division duplex systems, where uplink and downlink channels are reciprocal, the channel estimate procedure is exposed to attacks known as pilot contamination, with the aim of having an enhanced data signal sent to the malicious user. The Shifted 2-N-PSK method involves two random legitimate pilots in the training phase, each of which belongs to a constellation, shifted from the original N-PSK symbols by certain degrees. In this paper, legitimate pilots’ offset values and their influence on the detection capabilities of the Shifted 2-N-PSK method are investigated. As the implementation of the technique depends on the relation between the shift angles rather than their specific values, the optimal interconnection between the two legitimate constellations is investigated. The results show that no regularity exists in the relation between the pilot contamination attacks (PCA) detection probability and the choice of offset values. Therefore, an adversary who aims to obtain the exact offset values can only employ a brute-force attack but the large number of possible combinations for the shifted constellations makes such a type of attack difficult to successfully mount. For this reason, the number of optimal shift value pairs is also studied for both 100% and 98% probabilities of detecting pilot contamination attacks. Although the Shifted 2-N-PSK method has been broadly studied in different signal-to-noise ratio scenarios, in multi-cell systems the interference from the signals in other cells should be also taken into account. Therefore, the inter-cell interference impact on the performance of the method is investigated by means of a large number of simulations. The results show that the detection probability of the Shifted 2-N-PSK decreases inversely to the signal-to-interference-plus-noise ratio.

Keywords: channel estimation, inter-cell interference, pilot contamination attacks, wireless communications

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Authors: D. Sigirli

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The Receiver Operating Characteristic (ROC) curve is a commonly used statistical tool for evaluating the diagnostic performance of screening and diagnostic test with continuous or ordinal scale results which aims to predict the presence or absence probability of a condition, usually a disease. When the test results were measured as numeric values, sensitivity and specificity can be computed across all possible threshold values which discriminate the subjects as diseased and non-diseased. There are infinite numbers of possible decision thresholds along the continuum of the test results. The ROC curve presents the trade-off between sensitivity and the 1-specificity as the threshold changes. The empirical ROC curve which is a non-parametric estimator of the ROC curve is robust and it represents data accurately. However, especially for small sample sizes, it has a problem of variability and as it is a step function there can be different false positive rates for a true positive rate value and vice versa. Besides, the estimated ROC curve being in a jagged form, since the true ROC curve is a smooth curve, it underestimates the true ROC curve. Since the true ROC curve is assumed to be smooth, several smoothing methods have been explored to smooth a ROC curve. These include using kernel estimates, using log-concave densities, to fit parameters for the specified density function to the data with the maximum-likelihood fitting of univariate distributions or to create a probability distribution by fitting the specified distribution to the data nd using smooth versions of the empirical distribution functions. In the present paper, we aimed to propose a smooth ROC curve estimation based on the boundary corrected kernel function and to compare the performances of ROC curve smoothing methods for the diagnostic test results coming from different distributions in different sample sizes. We performed simulation study to compare the performances of different methods for different scenarios with 1000 repetitions. It is seen that the performance of the proposed method was typically better than that of the empirical ROC curve and only slightly worse compared to the binormal model when in fact the underlying samples were generated from the normal distribution.

Keywords: empirical estimator, kernel function, smoothing, receiver operating characteristic curve

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Authors: Mustafa GOK1, Sair Kahraman2, Mustafa FENER3

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In rock engineering applications, determining uniaxial compressive strength (UCS), Brazilian tensile strength (BTS), and basic index properties such as density, porosity, and water absorption is crucial for the design of both underground and surface structures. However, obtaining reliable samples for direct testing, especially from rocks that weather quickly and have low strength, is often challenging. In such cases, indirect methods provide a practical alternative to estimate the physical and mechanical properties of these rocks. In this study, tuff samples collected from the Cappadocia region (Nevşehir) in Turkey were subjected to indirect testing methods. Over 100 tests were conducted, using needle penetrometer index (NPI), point load strength index (PLI), and disc shear index (BPI) to estimate the uniaxial compressive strength (UCS), Brazilian tensile strength (BTS), density, and water absorption index of the tuffs. The relationships between the results of these indirect tests and the target physical properties were evaluated using simple and multiple regression analyses. The findings of this research reveal strong correlations between the indirect methods and the mechanical properties of the tuffs. Both uniaxial compressive strength and Brazilian tensile strength could be accurately predicted using NPI, PLI, and BPI values. The regression models developed in this study allow for rapid, cost-effective assessments of tuff strength in cases where direct testing is impractical. These results are particularly valuable for geological engineering applications, where time and resource constraints exist. This study highlights the significance of using indirect methods as reliable predictors of the mechanical behavior of weak rocks like tuffs. Further research is recommended to explore the application of these methods to other rock types with similar characteristics. Further research is required to compare the results with those of established direct test methods.

Keywords: brazilian tensile strength, disc shear strength, indirect methods, tuffs, uniaxial compressive strength

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Authors: Alaa Hamed Salama, Rehab Nabil Shamma

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Introduction: Polymeric micelles that can deliver drug to intended sites of the eye have attracted much scientific attention recently. The aim of this study was to review the aqueous-based formulation of drug-loaded polymeric micelles that hold significant promise for ophthalmic drug delivery. This study investigated the synergistic performance of mixed polymeric micelles made of linear and branched poly (ethylene oxide)-poly (propylene oxide) for the more effective encapsulation of Lornoxicam (LX) as a hydrophobic model drug. Methods: The co-micellization process of 10% binary systems combining different weight ratios of the highly hydrophilic poloxamers; Synperonic® PE/P84, and Synperonic® PE/F127 and the hydrophobic poloxamine counterpart (Tetronic® T701) was investigated by means of photon correlation spectroscopy and cloud point. The drug-loaded micelles were tested for their solubilizing capacity towards LX. Results: Results showed a sharp solubility increase from 0.46 mg/ml up to more than 4.34 mg/ml, representing about 136-fold increase. Optimized formulation was selected to achieve maximum drug solubilizing power and clarity with lowest possible particle size. The optimized formulation was characterized by 1HNMR analysis which revealed complete encapsulation of the drug within the micelles. Further investigations by histopathological and confocal laser studies revealed the non-irritant nature and good corneal penetrating power of the proposed nano-formulation. Conclusion: LX-loaded polymeric nanomicellar formulation was fabricated allowing easy application of the drug in the form of clear eye drops that do not cause blurred vision or discomfort, thus achieving high patient compliance.

Keywords: confocal laser scanning microscopy, Histopathological studies, Lornoxicam, micellar solubilization

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Authors: Agoston Restas

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Introduction: This paper describes many initiatives and shows also practical examples which happened recently using Unmanned Aerial Systems (UAS) to support disaster management. Since the operation of manned aircraft at disasters is usually not only expensive but often impossible to use as well, in many cases managers fail to use the aerial activity. UAS can be an alternative moreover cost-effective solution for supporting disaster management. Methods: This article uses thematic division of UAS applications; it is based on two key elements, one of them is the time flow of managing disasters, other is its tactical requirements. Logically UAS can be used like pre-disaster activity, activity immediately after the occurrence of a disaster and the activity after the primary disaster elimination. Paper faces different disasters, like dangerous material releases, floods, earthquakes, forest fires and human-induced disasters. Research used function analysis, practical experiments, mathematical formulas, economic analysis and also expert estimation. Author gathered international examples and used own experiences in this field as well. Results and discussion: An earthquake is a rapid escalating disaster, where, many times, there is no other way for a rapid damage assessment than aerial reconnaissance. For special rescue teams, the UAS application can help much in a rapid location selection, where enough place remained to survive for victims. Floods are typical for a slow onset disaster. In contrast, managing floods is a very complex and difficult task. It requires continuous monitoring of dykes, flooded and threatened areas. UAS can help managers largely keeping an area under observation. Forest fires are disasters, where the tactical application of UAS is already well developed. It can be used for fire detection, intervention monitoring and also for post-fire monitoring. In case of nuclear accident or hazardous material leakage, UAS is also a very effective or can be the only one tool for supporting disaster management. Paper shows some efforts using UAS to avoid human-induced disasters in low-income countries as part of health cooperation.

Keywords: disaster management, floods, forest fires, Unmanned Aerial Systems

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Authors: Hernán A. Perez, Luis J. Armando, Néstor H. García

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Background and aims Cardiovascular disease rates are very high in patients with renal failure (CRF), but the underlying mechanisms are incompletely understood. Traditional cardiovascular risk factors do not explain the increased risk, and observational studies have observed paradoxical or absent associations between classical risk factors and mortality in dialysis patients. A large randomized controlled trial, the 4D Study, the AURORA and the ALERT study found that statin therapy in CRF do not reduce cardiovascular events. These results may be the results of ‘accelerated atherosclerosis’ observed on these patients. The objective of this study was to investigate if carotid total plaque area (TPA), a measure of carotid plaque burden growth is increased at progressively lower creatinine clearance in patients with CRF. We studied a cohort of patients with CRF not on dialysis, reasoning that risk factor associations might be more easily discerned before end stage renal disease. Methods: The Blossom DMO Argentina ethics committee approved the study and informed consent from each participant was obtained. We performed a cohort study in 412 patients with Stage 1, 2 and 3 CRF. Clinical and laboratory data were obtained. TPA was determined using bilateral carotid ultrasonography. Modification of Diet in Renal Disease estimation formula was used to determine renal function. ANOVA was used when appropriate. Results: Stage 1 CRF group (n= 16, 43±2yo) had a blood pressure of 123±2/78±2 mmHg, BMI 30±1, LDL col 145±10 mg/dl, HbA1c 5.8±0.4% and had the lowest TPA 25.8±6.9 mm2. Stage 2 CRF (n=231, 50±1 yo) had a blood pressure of 132±1/81±1 mmHg, LDL col 125±2 mg/dl, HbA1c 6±0.1% and TPA 48±10mm2 ( p< 0.05 vs CRF stage 1) while Stage 3 CRF (n=165, 59±1 yo) had a blood pressure of 134±1/81±1, LDL col 125±3 mg/dl, HbA1c 6±0.1% and TPA 71±6mm2 (p < 0.05 vs CRF stage 1 and 2). Conclusion: Our data indicate that TPA increases along the renal function deterioration, and it is not related with the LDL cholesterol and triglycerides levels. We suggest that mechanisms other than the classics are responsible for the observed excess of cardiovascular disease in CKD patients and finally, determination of total plaque area should be used to measure effects of antiatherosclerotic therapy.

Keywords: hypertension, chronic renal failure, atherosclerosis, cholesterol

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Authors: Tuba Zahra, Aakash Parekh

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Hyperspectral remote sensing refers to imaging of objects or materials in narrow conspicuous spectral bands. Hyperspectral images (HSI) enable the extraction of spectral signatures for objects or materials observed. These images contain information about the reflectance of each pixel across the electromagnetic spectrum. It enables the acquisition of data simultaneously in hundreds of spectral bands with narrow bandwidths and can provide detailed contiguous spectral curves that traditional multispectral sensors cannot offer. The contiguous, narrow bandwidth of hyperspectral data facilitates the detailed surveying of Earth's surface features. This would otherwise not be possible with the relatively coarse bandwidths acquired by other types of imaging sensors. Hyperspectral imaging provides significantly higher spectral and spatial resolution. There are several use cases that represent the commercial applications of hyperspectral remote sensing. Each use case represents just one of the ways that hyperspectral satellite imagery can support operational efficiency in the respective vertical. There are some use cases that are specific to VNIR bands, while others are specific to SWIR bands. This paper discusses the different commercially viable use cases that are significant for HSI application areas, such as agriculture, mining, oil and gas, defense, environment, and climate, to name a few. Theoretically, there is n number of use cases for each of the application areas, but an attempt has been made to streamline the use cases depending upon economic feasibility and commercial viability and present a review of literature from this perspective. Some of the specific use cases with respect to agriculture are crop species (sub variety) detection, soil health mapping, pre-symptomatic crop disease detection, invasive species detection, crop condition optimization, yield estimation, and supply chain monitoring at scale. Similarly, each of the industry verticals has a specific commercially viable use case that is discussed in the paper in detail.

Keywords: agriculture, mining, oil and gas, defense, environment and climate, hyperspectral, VNIR, SWIR

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Authors: M. Miljkovic, A. Urakpo, M. Simic-Koumoutsaris

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Benefit-risk assessment (BRA) is a valuable tool that takes place in multiple stages during a medicine's lifecycle, and this assessment can be conducted in a variety of ways. The aim was to summarize current BRA methods used during approval decisions and in post-approval settings and to see possible future directions. Relevant reviews, recommendations, and guidelines published in medical literature and through regulatory agencies over the past five years have been examined. BRA implies the review of two dimensions: the dimension of benefits (determined mainly by the therapeutic efficacy) and the dimension of risks (comprises the safety profile of a drug). Regulators, industry, and academia have developed various approaches, ranging from descriptive textual (qualitative) to decision-analytic (quantitative) models, to facilitate the BRA of medicines during the product lifecycle (from Phase I trials, to authorization procedure, post-marketing surveillance and health technology assessment for inclusion in public formularies). These approaches can be classified into the following categories: stepwise structured approaches (frameworks); measures for benefits and risks that are usually endpoint specific (metrics), simulation techniques and meta-analysis (estimation techniques), and utility survey techniques to elicit stakeholders’ preferences (utilities). All these approaches share the following two common goals: to assist this analysis and to improve the communication of decisions, but each is subject to its own specific strengths and limitations. Before using any method, its utility, complexity, the extent to which it is established, and the ease of results interpretation should be considered. Despite widespread and long-time use, BRA is subject to debate, suffers from a number of limitations, and currently is still under development. The use of formal, systematic structured approaches to BRA for regulatory decision-making and quantitative methods to support BRA during the product lifecycle is a standard practice in medicine that is subject to continuous improvement and modernization, not only in methodology but also in cooperation between organizations.

Keywords: benefit-risk assessment, benefit-risk profile, product lifecycle, quantitative methods, structured approaches

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Authors: M. P. Tripathi, Priti Tiwari

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Soil and Water Assessment Tool (SWAT) is a distributed parameter continuous time model and was tested on daily and fortnightly basis for a small agricultural watershed (Dhangaon) of Chhattisgarh state in India. The SWAT model recently interfaced with ArcGIS and called as ArcSWAT. The watershed and sub-watershed boundaries, drainage networks, slope and texture maps were generated in the environment of ArcGIS of ArcSWAT. Supervised classification method was used for land use/cover classification from satellite imageries of the years 2009 and 2012. Manning's roughness coefficient 'n' for overland flow and channel flow and Fraction of Field Capacity (FFC) were calibrated for monsoon season of the years 2009 and 2010. The model was validated on a daily basis for the years 2011 and 2012 by using the observed daily rainfall and temperature data. Calibration and validation results revealed that the model was predicting the daily surface runoff and sediment yield satisfactorily. Sensitivity analysis showed that the annual sediment yield was inversely proportional to the overland and channel 'n' values whereas; annual runoff and sediment yields were directly proportional to the FFC. The model was also tested (calibrated and validated) for the fortnightly runoff and sediment yield for the year 2009-10 and 2011-12, respectively. Simulated values of fortnightly runoff and sediment yield for the calibration and validation years compared well with their observed counterparts. The calibration and validation results revealed that the ArcSWAT model could be used for identification of critical sub-watershed and for developing management scenarios for the Dhangaon watershed. Further, the model should be tested for simulating the surface runoff and sediment yield using generated rainfall and temperature before applying it for developing the management scenario for the critical or priority sub-watersheds.

Keywords: watershed, hydrologic and water quality, ArcSWAT model, remote sensing, GIS, runoff and sediment yield

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Authors: Roengchai Tansuchat, Woraphon Yamaka, Paravee Maneejuk

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From the past recent, the global financial crisis, economic instability, and large fluctuation in agricultural commodity price have led to increased concerns about the volatility transmission among them. The problem is further exacerbated by commodities volatility caused by other commodity price fluctuations, hence the decision on hedging strategy has become both costly and useless. Thus, this paper is conducted to analysis the volatility spillover effect among major agriculture including corn, soybeans, wheat and rice, to help the commodity suppliers hedge their portfolios, and manage the risk and co-volatility of them. We provide a switching regime approach to analyzing the issue of volatility spillovers in different economic conditions, namely upturn and downturn economic. In particular, we investigate relationships and volatility transmissions between these commodities in different economic conditions. We purposed a Copula-based multivariate Markov Switching GARCH model with two regimes that depend on an economic conditions and perform simulation study to check the accuracy of our proposed model. In this study, the correlation term in the cross-hedge ratio is obtained from six copula families – two elliptical copulas (Gaussian and Student-t) and four Archimedean copulas (Clayton, Gumbel, Frank, and Joe). We use one-step maximum likelihood estimation techniques to estimate our models and compare the performance of these copula using Akaike information criterion (AIC) and Bayesian information criteria (BIC). In the application study of agriculture commodities, the weekly data used are conducted from 4 January 2005 to 1 September 2016, covering 612 observations. The empirical results indicate that the volatility spillover effects among cereal futures are different, as response of different economic condition. In addition, the results of hedge effectiveness will also suggest the optimal cross hedge strategies in different economic condition especially upturn and downturn economic.

Keywords: agricultural commodity futures, cereal, cross-hedge, spillover effect, switching regime approach

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Authors: Marzieh Khedmati, Shannon L. Bartelt-Hunt

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Vegetative Filter Strips (VFS) are used for controlling the volume of runoff and decreasing contaminant concentrations in runoff before entering water bodies. Many studies have investigated the role of VFS in sediment and nutrient removal, but little is known about their efficiency for the removal of emerging contaminants such as antimicrobial resistance genes (ARGs). Vegetative Filter Strip Modeling System (VFSMOD) was used to simulate the efficiency of VFS in this regard. Several studies demonstrated the ability of VFSMOD to predict reductions in runoff volume and sediment concentration moving through the filters. The objectives of this study were to calibrate the VFSMOD with experimental data and assess the efficiency of the model in simulating the filter behavior in removing ARGs (ermB) and tylosin. The experimental data were obtained from a prior study conducted at the University of Nebraska (UNL) Rogers Memorial Farm. Three treatment factors were tested in the experiments, including manure amendment, narrow grass hedges and rainfall events. Sediment Delivery Ratio (SDR) was defined as the filter efficiency and the related experimental and model values were compared to each other. The VFS Model generally agreed with the experimental results and as a result, the model was used for predicting filter efficiencies when the runoff data are not available. Narrow Grass Hedges (NGH) were shown to be effective in reducing tylosin and ARGs concentration. The simulation showed that the filter efficiency in removing ARGs is different for different soil types and filter lengths. There is an optimum length for the filter strip that produces minimum runoff volume. Based on the model results increasing the length of the filter by 1-meter leads to higher efficiency but widening beyond that decreases the efficiency. The VFSMOD, which was proved to work well in estimation of VFS trapping efficiency, showed confirming results for ARG removal.

Keywords: antimicrobial resistance genes, emerging contaminants, narrow grass hedges, vegetative filter strips, vegetative filter strip modeling system

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Authors: Shahrzad Zolfagharnassab, Abdul Rashid Mohamed Shariff, Reza Ehsani, Hawa Ze Jaffar, Ishak Aris

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Ripeness estimation of oil palm fresh fruit is important processes that affect the profitableness and salability of oil palm fruits. The adulthood or ripeness of the oil palm fruits influences the quality of oil palm. Conventional procedure includes physical grading of Fresh Fruit Bunches (FFB) maturity by calculating the number of loose fruits per bunch. This physical classification of oil palm FFB is costly, time consuming and the results may have human error. Hence, many researchers try to develop the methods for ascertaining the maturity of oil palm fruits and thereby, deviously the oil content of distinct palm fruits without the need for exhausting oil extraction and analysis. This research investigates the potential of infrared images (Thermal Images) as a predictor to classify the oil palm FFB ripeness. A total of 270 oil palm fresh fruit bunches from most common cultivar of oil palm bunches Nigresens according to three maturity categories: under ripe, ripe and over ripe were collected. Each sample was scanned by the thermal imaging cameras FLIR E60 and FLIR T440. The average temperature of each bunches were calculated by using image processing in FLIR Tools and FLIR ThermaCAM researcher pro 2.10 environment software. The results show that temperature content decreased from immature to over mature oil palm FFBs. An overall analysis-of-variance (ANOVA) test was proved that this predictor gave significant difference between underripe, ripe and overripe maturity categories. This shows that the temperature as predictors can be good indicators to classify oil palm FFB. Classification analysis was performed by using the temperature of the FFB as predictors through Linear Discriminant Analysis (LDA), Mahalanobis Discriminant Analysis (MDA), Artificial Neural Network (ANN) and K- Nearest Neighbor (KNN) methods. The highest overall classification accuracy was 88.2% by using Artificial Neural Network. This research proves that thermal imaging and neural network method can be used as predictors of oil palm maturity classification.

Keywords: artificial neural network, maturity classification, oil palm FFB, thermal imaging

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Authors: Luis Alvarado, Victor Poblete, Isaac Gonzalez, Yetzabeth Gonzalez

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In MIREX 2016 (http://www.music-ir.org/mirex), the deep neural network (DNN)-Deep Chroma Extractor, proposed by Korzeniowski and Wiedmer, reached the highest score in an audio chord recognition task. In the present paper, this tool is assessed under acoustic reverberant environments and distinct source-microphone distances. The evaluation dataset comprises The Beatles and Queen datasets. These datasets are sequentially re-recorded with a single microphone in a real reverberant chamber at four reverberation times (0 -anechoic-, 1, 2, and 3 s, approximately), as well as four source-microphone distances (32, 64, 128, and 256 cm). It is expected that the performance of the trained DNN will dramatically decrease under these acoustic conditions with signals degraded by room reverberation and distance to the source. Recently, the effect of the bio-inspired Locally-Normalized Cepstral Coefficients (LNCC), has been assessed in a text independent speaker verification task using speech signals degraded by additive noise at different signal-to-noise ratios with variations of recording distance, and it has also been assessed under reverberant conditions with variations of recording distance. LNCC showed a performance so high as the state-of-the-art Mel Frequency Cepstral Coefficient filters. Based on these results, this paper proposes a variation of locally-normalized triangular filters called Locally-Normalized Quarter Tone (LNQT) filters. By using the LNQT spectrogram, robustness improvements of the trained Deep Chroma Extractor are expected, compared with classical triangular filters, and thus compensating the music signal degradation improving the accuracy of the chord recognition system.

Keywords: chord recognition, deep neural networks, feature extraction, music information retrieval

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