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In this work, the effects of different fiber loadings on the mechanical properties of the composites at the sub-micron scale were studied through nanoindentation followed by physical characterization. The composites were prepared by incorporating different loadings of wheat straw, corn stalk, and rice husk in polypropylene copolymer using a melt processing method followed by thermal–hydraulic compression technique. Nanoindentation experiments in quasi-continuous stiffness mode were performed on the surfaces of produced composites to study the composites’ elastic modulus, hardness, and creep properties. The obtained results expressed the in-depth study of the micro- and macro-level structure and behavior of particle interactions. The findings demonstrated that observable shifts in composites’ hardness, elastic modulus, and creep rate had occurred. The WS-reinforced biocomposite sheet showed the highest elastic modulus of 1.09 and hardness of 0.11 GPa at 40 wt% loading in comparison to other loadings. An impact strength of 7.55 kJ/m2 was noted for the biocomposite at 40 wt% RH loading. In addition, optical microscopy, Fourier transform infrared spectroscopy, water absorption, thickness swelling, and Vicat softening point studies were conducted on biocomposite sheets to evaluate differences in physical, mechanical, and thermal properties. The outstanding mechanical performance of the newly developed composites makes them suitable for use as a biodegradable packaging material.

The hike in fuel prices and rapid depletion of fuel reserves have compelled scientists to focus on energy conservation, environmental protection, engine performance improvement, and cost saving. The prime objective of the study is to compare the empirical results with response surface methodology (RSM) optimized results in order to check the accuracy of model designed by RSM. Therefore, the current study examines the effect of fuel injection parameters (nozzle opening pressure and protrusion) on diesel engine performance and exhaust emissions. RSM technique was applied to predict engine performance and exhaust emission parameters along with their optimization. The brake thermal efficiency (BTE) was incremented by 1.23 % for protrusion from 1.5 to 2.5 mm under 240 bar nozzle opening pressure (NOP). BTE was increased by 0.94 and 4.51 % for 1.5 and 2.5 mm protrusion respectively. CO emission was decremented by 4.47 and 11.31 % for 1.5 and 2.5 mm protrusion respectively when the NOP changed from 230 to 240 bar. RSM model optimized input conditions 240 bar pressure, 2.5 mm protrusion, and 1935.67 engine rpm. The engine was again tested on RSM-optimized conditions and the highest absolute percentage error (APE) of 4.42 % was obtained for NOx emission, while the lowest APE of 2.89 % was obtained for BSFC.

Highly concentrated triple-junction solar cells (HCTJSCs) are cells that have diverse applications for power generation. Their electrical efficiency is almost 45%, which may be increased to 50% by the end of the year 2030. Despite their overwhelming ability to generate power, their efficiency is lower when utilized in a concentrated manner, which introduces a high-temperature surge, leading to a sudden drop in output power. In this study, the efficiency of a 10 mm × 10 mm multijunction solar cell (MJSC) was increased to almost 42% under the climatic conditions in Lahore, Pakistan. Active cooling was selected, where SiO2–water- and Al2O3–water-based nanofluids with varying volume fractions, ranging from 5% to 15% by volume, were used with a 0.001 kg/s mass flow rate. In addition, two- and three-layer microchannel heat sinks (MCHSs) with squared microchannels were designed to perform thermal management. Regarding the concentration ratio, 1500 suns were considered for 15 August at noon, with 805 W/m2 and 110 W/m2 direct and indirect radiation, respectively. A complete model including a triple-junction solar cell and allied assemblies was modeled in Solidworks software, followed by temperature profile generation in steady-state thermal analyses (SSTA). Thereafter, a coupling of SSTA and Ansys Fluent was made, in combination with the thermal management of the entire model, where the temperature of the TJSC was found to be 991 °C without active cooling, resulting in a decrease in electrical output. At 0.001 kg/s, the optimum average surface temperature (44.5 °C), electrical efficiency (41.97%), and temperature uniformity (16.47 °C) were achieved in the of MJSC with SiO2–water nanofluid with three layers of MCHS at a 15% volume fraction. Furthermore, the average outlet temperature of the Al2O3–water nanofluid at all volume fractions was high, between 29.53 °C and 31.83 °C, using the two-layer configuration. For the three-layer arrangement, the input and output temperatures

Highly concentrated triple-junction solar cells (HCTJSCs) are cells that have diverse applications for power generation. Their electrical efficiency is almost 45%, which may be increased to 50% by the end of the year 2030. Despite their overwhelming ability to generate power, their efficiency is lower when utilized in a concentrated manner, which introduces a high-temperature surge, leading to a sudden drop in output power. In this study, the efficiency of a 10 mm × 10 mm multijunction solar cell (MJSC) was increased to almost 42% under the climatic conditions in Lahore, Pakistan. Active cooling was selected, where SiO2–water- and Al2O3–water-based nanofluids with varying volume fractions, ranging from 5% to 15% by volume, were used with a 0.001 kg/s mass flow rate. In addition, two- and three-layer microchannel heat sinks (MCHSs) with squared microchannels were designed to perform thermal management. Regarding the concentration ratio, 1500 suns were considered for 15 August at noon, with 805 W/m2 and 110 W/m2 direct and indirect radiation, respectively. A complete model including a triple-junction solar cell and allied assemblies was modeled in Solidworks software, followed by temperature profile generation in steady-state thermal analyses (SSTA). Thereafter, a coupling of SSTA and Ansys Fluent was made, in combination with the thermal management of the entire model, where the temperature of the TJSC was found to be 991 °C without active cooling, resulting in a decrease in electrical output. At 0.001 kg/s, the optimum average surface temperature (44.5 °C), electrical efficiency (41.97%), and temperature uniformity (16.47 °C) were achieved in the of MJSC with SiO2–water nanofluid with three layers of MCHS at a 15% volume fraction. Furthermore, the average outlet temperature of the Al2O3–water nanofluid at all volume fractions was high, between 29.53 °C and 31.83 °C, using the two-layer configuration. For the three-layer arrangement, the input and output temperatures

The purpose of this study is to investigate the potential use of solar energy within an oil refinery to reduce its fossil fuel consumption and greenhouse gas emissions. A validated ASPEN HYSYS model was used to investigate the products produced from heavy crude oil in the refinery. Using TRNSYS software, the proposed Parabolic Trough Collector (PTC)-based solar heating system paired with the boiler is modelled. Sensible thermal energy storage (TES) system is integrated into the refinery's process heating to handle the intermittent nature of solar energy. It was discovered that 463 m2 of the PTC area coupled with a 15000-L TES tank can result in a maximum life cycle cost savings of 21.046 thousand USD for an annual heat supply of 116,944 MWh to generate steam at a temperature of 200–220 °C. In the proposed hybrid heating system, the yearly solar fraction is determined to be 26.99% and the payback period is 8.77 years with the average solar irradiance of 900 W/m2. In addition, the system can yearly reduce greenhouse gas (GHG) emissions by about 34.045 tonnes of CO2 equivalents.

Growing environmental concerns over global warming and depleting fossil fuel reserves are compelling researchers to investigate green fuels such as alcoholic fuels that not only show the concrete decrement in emissions but also enhance engine performance. The current study emphasizes the influence of different alcoholic fuel blends in gasoline on engine performance and emissions for an engine speed ranging from 1200 to 4400 rpm. The obtained performance results demonstrate that the brake power and brake thermal efficiency (BTE) increased with an incrementing blend percentage of ethanol and methanol in gasoline (EM). The minimum brake specific fuel consumption (BSFC) was ascertained using pure gasoline followed by E2 and then E5M5. The NOx and CO2 emissions can be described in the decreasing order of E, EM and gasoline due to same trend of exhaust gas temperature (EGT). CO results were in reverse order of CO2. HC emissions were found in the increasing order of E, EM and pure gasoline. E10 performed better among all blends in terms of less exhaust emissions and engine performance. However, EM blended with gasoline significantly reduced NOx. E5M5 produced 1.9% lower NOx emission compared to E10 owing to 1.2% lower EGT. Moreover, greenhouse gases such as CO2, which is mainly responsible for global warming reducing by 1.1% in case E5M5 as compared to E10.

In this work, the effects of different fiber loadings on the mechanical properties of the composites at the sub-micron scale were studied through nanoindentation followed by physical characterization. The composites were prepared by incorporating different loadings of wheat straw, corn stalk, and rice husk in polypropylene copolymer using a melt processing method followed by thermal–hydraulic compression technique. Nanoindentation experiments in quasi-continuous stiffness mode were performed on the surfaces of produced composites to study the composites’ elastic modulus, hardness, and creep properties. The obtained results expressed the in-depth study of the micro- and macro-level structure and behavior of particle interactions. The findings demonstrated that observable shifts in composites’ hardness, elastic modulus, and creep rate had occurred. The WS-reinforced biocomposite sheet showed the highest elastic modulus of 1.09 and hardness of 0.11 GPa at 40 wt% loading in comparison to other loadings. An impact strength of 7.55 kJ/m2 was noted for the biocomposite at 40 wt% RH loading. In addition, optical microscopy, Fourier transform infrared spectroscopy, water absorption, thickness swelling, and Vicat softening point studies were conducted on biocomposite sheets to evaluate differences in physical, mechanical, and thermal properties. The outstanding mechanical performance of the newly developed composites makes them suitable for use as a biodegradable packaging material.

Polymer-based nanocomposites are being considered as replacements for conventional materials in medium to high-temperature applications. This article aims to discover the synergistic effects of reinforcements on the developed polymer-based nanocomposite. An epoxy-based polymer composite was manufactured by reinforcing graphene nanoplatelets (GNP) and h-boron nitride (h-BN) nanofillers. The composites were prepared by varying the reinforcements with the step of 0.1 from 0.1 to 0.6%. Ultrasonication was carried out to ensure the homogenous dispersion of reinforcements. Mechanical, thermal, functional, and scanning electron microscopy (SEM) analysis was carried out on the novel manufactured composites. The evaluation revealed that the polymer composite with GNP 0.2 by wt % has shown an increase in load-bearing capacity by 265% and flexural strength by 165% compared with the pristine form, and the polymer composite with GNP and h-BN 0.6 by wt % showed an increase in load-bearing capacity by 219% and flexural strength by 114% when compared with the pristine form. Furthermore, the evaluation showed that the novel prepared nanocomposite reinforced with GNP and h-BN withstands a higher temperature, around 340 °C, which is validated by thermogravimetric analysis (TGA) trials. The numerical simulation model is implemented to gather the synthesised nanocomposite's best composition and mechanical properties. The minor error between the simulation and experimental data endorses the model's validity. To demonstrate the industrial applicability of the presented material, a case study is proposed to predict the temperature range for compressor blades of gas turbine engines containing nanocomposite material as the substrate and graphene/h-BN as reinforcement particles.

Polymer-based nanocomposites are being considered as replacements for conventional materials in medium to high-temperature applications. This article aims to discover the synergistic effects of reinforcements on the developed polymer-based nanocomposite. An epoxy-based polymer composite was manufactured by reinforcing graphene nanoplatelets (GNP) and h-boron nitride (h-BN) nanofillers. The composites were prepared by varying the reinforcements with the step of 0.1 from 0.1 to 0.6%. Ultrasonication was carried out to ensure the homogenous dispersion of reinforcements. Mechanical, thermal, functional, and scanning electron microscopy (SEM) analysis was carried out on the novel manufactured composites. The evaluation revealed that the polymer composite with GNP 0.2 by wt % has shown an increase in load-bearing capacity by 265% and flexural strength by 165% compared with the pristine form, and the polymer composite with GNP and h-BN 0.6 by wt % showed an increase in load-bearing capacity by 219% and flexural strength by 114% when compared with the pristine form. Furthermore, the evaluation showed that the novel prepared nanocomposite reinforced with GNP and h-BN withstands a higher temperature, around 340 °C, which is validated by thermogravimetric analysis (TGA) trials. The numerical simulation model is implemented to gather the synthesised nanocomposite's best composition and mechanical properties. The minor error between the simulation and experimental data endorses the model's validity. To demonstrate the industrial applicability of the presented material, a case study is proposed to predict the temperature range for compressor blades of gas turbine engines containing nanocomposite material as the substrate and graphene/h-BN as reinforcement particles.

The prevailing massive exploitation of conventional fuels has staked the energy accessibility to future generations. The gloomy peril of inflated demand and depleting fuel reservoirs in the energy sector has supposedly instigated the urgent need for reliable alternative fuels. These very issues have been addressed by introducing oxyhydrogen gas (HHO) in compression ignition (CI) engines in various flow rates with diesel for assessing brake-specific fuel consumption (BSFC) and brake thermal efficiency (BTE). The enrichment of neat diesel fuel with 10 dm3/min of HHO resulted in the most substantial decrease in BSFC and improved BTE at all test speeds in the range of 1000– 2200 rpm. Moreover, an Artificial Intelligence (AI) approach was employed for designing an ANN performance-predicting model with an engine operating on HHO. The correlation coefficients (R) of BSFC and BTE given by the ANN predicting model were 0.99764 and 0.99902, respectively. The mean root errors (MRE) of both parameters (BSFC and BTE) were within the range of 1%–3% while the root mean square errors (RMSE) were 0.0122 kg/kWh and 0.2768% for BSFC and BTE, respec-tively. In addition, ANN was coupled with the response surface methodology (RSM) technique for comprehending the individual impact of design parameters and their statistical interactions gov-erning the output parameters. The R2 values of RSM responses (BSFC and BTE) were near to 1 and MRE values were within the designated range. The comparative evaluation of ANN and RSM predicting models revealed that MRE and RMSE of RSM models are also well within the desired range but to be outrightly accurate and precise, the choice of ANN should be potentially endorsed. Thus, the combined use of ANN and RSM could be used effectively for reliable predictions and effective study of statistical interactions.

The prevailing massive exploitation of conventional fuels has staked the energy accessibility to future generations. The gloomy peril of inflated demand and depleting fuel reservoirs in the energy sector has supposedly instigated the urgent need for reliable alternative fuels. These very issues have been addressed by introducing oxyhydrogen gas (HHO) in compression ignition (CI) engines in various flow rates with diesel for assessing brake-specific fuel consumption (BSFC) and brake thermal efficiency (BTE). The enrichment of neat diesel fuel with 10 dm3/min of HHO resulted in the most substantial decrease in BSFC and improved BTE at all test speeds in the range of 1000– 2200 rpm. Moreover, an Artificial Intelligence (AI) approach was employed for designing an ANN performance-predicting model with an engine operating on HHO. The correlation coefficients (R) of BSFC and BTE given by the ANN predicting model were 0.99764 and 0.99902, respectively. The mean root errors (MRE) of both parameters (BSFC and BTE) were within the range of 1%–3% while the root mean square errors (RMSE) were 0.0122 kg/kWh and 0.2768% for BSFC and BTE, respec-tively. In addition, ANN was coupled with the response surface methodology (RSM) technique for comprehending the individual impact of design parameters and their statistical interactions gov-erning the output parameters. The R2 values of RSM responses (BSFC and BTE) were near to 1 and MRE values were within the designated range. The comparative evaluation of ANN and RSM predicting models revealed that MRE and RMSE of RSM models are also well within the desired range but to be outrightly accurate and precise, the choice of ANN should be potentially endorsed. Thus, the combined use of ANN and RSM could be used effectively for reliable predictions and effective study of statistical interactions.

In many remote localities, one of the underlying reasons for not receiving life-saving vaccines is the lack of electricity to store the vaccines in the required refrigerated conditions. Solar Photovoltaic (PV) refrigerators have been considered as a viable and green solution to store the vaccines in remote localities having no access to electricity. In this paper, a detailed methodology has been presented for the performance evaluation of a solar PV powered vaccine refrigerator for remote locations. Thermal modelling with hourly cooling load calculations and refrigeration cycle simulations were carried out. The performance parameters for three environment-friendly refrigerants: R152a, R1234yf, and R1234ze(E) has been compared against the commonly used R134a for two remote, off-grid locations in Bangladesh and South Sudan. The energy systems comprising of solar PV panels and batteries to run the refrigerator were modelled in HOMER software for techno-economic optimizations. For both the locations, R152a was found to be the best performing refrigerant exhibiting higher COP (2%−5.29%) as compared to the other refrigerants throughout the year, while R1234ze(E) exhibited COPs on par with R134a, and R1234yf had the least performance. Techno-economic analysis showed an energy system providing electricity to the refrigerator with R152a also had lower levelized cost of electricity (0.48%−2.54%) than the systems having other refrigerants in these locations.

Kalina cycle system (KCS) offers an attractive prospect to produce power by utilizing low-grade heat sources where traditional power cycles cannot be implemented. Intending to explore the potential of exploiting low-grade heat sources for conversion to electrical energy, this study proposes two modified power generation cycles based on KCS-34. A multi-phase expander is positioned between the Kalina separator and the second heat regenerator in the proposed X-modification. In contrast, it is located between the mixer and second regenerator for Y-modification. To explore the potential benefits and limitations of the proposed modifications contrasted with the KCS-34, thermodynamic modeling and optimization have been conducted. The influence of critical decision parameters on overall cycle performance is analyzed. The result elucidates that by implementing an additional multi-phase expander, a significant amount of energy can be extracted from a lean ammonia water loop and X-modification can deliver superior thermodynamic performance compared with the Y-modification and the original KCS-34. With a reduced turbine inlet pressure of 58 bar and an ammonia concentration of 80%, the X-modified cycle's efficiency reaches a peak value of 17% and a net power yield of 1015 kW. An increase of 6.35% can be achieved compared with the conventional KCS-34 operating at the same conditions. Maximum exergy destruction of the working substance was observed in the condenser.

The protection of the environment and pollution control are issues of paramount impor-tance. Researchers today are engrossed in mitigating the harmful impacts of petroleum waste on the environment. Lubricating oils, which are essential for the smooth operation of engines, are often disposed of improperly after completing their life. In the experimental work presented in this paper, deteriorated engine oil was regenerated using the acid treatment method and was reused in the engine. The comparison of the properties of reused oil, the engine’s performance, and the emissions from the engine are presented. The reuse of regenerated oil, the evaluation of performance, and emissions establish the usefulness of the regeneration of waste lubricating oil. For the used oil, total acid number (TAN), specific gravity, flash point, ash content, and kinematic viscosity changed by 60.7%, 6.7%, 4.4%, 96%, and 15.5%, respectively, compared with fresh oil. The regeneration partially restored all the lost lubricating oil properties. The performance parameters, brake power (BP), brake specific fuel consumption (BSFC), and exhaust gas temperature (EGT) improved with regenerated oil in use compared with used oil. The emissions CO and NOX contents for acid-treated oil were 9.7% and 17.3% less in comparison with used oil, respectively. Thus, regenerated oil showed improved performance and oil properties along with significantly reduced emissions when employed in an SI engine.

This paper proposes a computer assisted diagnostic (CAD) system for the detection of melanoma in dermoscopy images. Clinical findings have concluded that in case of melanoma, the lesion borders exhibit differential structures such as pigment networks and streaks as opposed to normal skin spots, which have smoother borders. We aim to validate these findings by performing segmentation of the skin lesions followed by an extraction of the peripheral region of the lesion that is subjected to feature extraction and classification for detecting melanoma. For segmentation, we propose a novel active contours based method that takes an initial lesion contour followed by the usage of Kullback-Leibler divergence between the lesion and skin to fit a curve precisely to the lesion boundaries. After segmentation of the lesion, its periphery is extracted to detect melanoma using image features that are based on local binary patterns. For validation of our algorithms, we have used the publicly available PH < formula > < tex > $^{2}$ < /tex > < /formula > dermoscopy dataset. An extensive experimental analysis reveals two important findings: 1). The proposed segmentation method mimics the ground truth data accurately, outperforming the other methods that have been used for comparison purposes, and 2). The most significant melanoma characteristics in the lesion actually lie on the lesion periphery.

This paper proposes a computer assisted diagnostic (CAD) system for the detection of melanoma in dermoscopy images. Clinical findings have concluded that in case of melanoma, the lesion borders exhibit differential structures such as pigment networks and streaks as opposed to normal skin spots, which have smoother borders. We aim to validate these findings by performing segmentation of the skin lesions followed by an extraction of the peripheral region of the lesion that is subjected to feature extraction and classification for detecting melanoma. For segmentation, we propose a novel active contours based method that takes an initial lesion contour followed by the usage of Kullback-Leibler divergence between the lesion and skin to fit a curve precisely to the lesion boundaries. After segmentation of the lesion, its periphery is extracted to detect melanoma using image features that are based on local binary patterns. For validation of our algorithms, we have used the publicly available PH < formula > < tex > $^{2}$ < /tex > < /formula > dermoscopy dataset. An extensive experimental analysis reveals two important findings: 1). The proposed segmentation method mimics the ground truth data accurately, outperforming the other methods that have been used for comparison purposes, and 2). The most significant melanoma characteristics in the lesion actually lie on the lesion periphery.

Power transformers are valuable assets in the power network, with the primary function to regulate the transmission and distribution voltage. The regulating device is the tap changer which sets the turn ratio. Tap changer failure is one of the leading causes of high-voltage power transformer failure. Most tap changer failures are caused by degrading contacts. Contact degradation may be the result of contact wear or of arcing-induced carbon deposition during on-load-tap (OLT) changes. Contact degradation increases contact resistance, causing increased heating and arcing, eventually leading to possible failure of the power transformer. Proper maintenance, therefore, requires a diagnostic system which can assess the condition of the contacts. The research presented is aiming at automated defect recognition and localisation from measured dynamic resistance measurement patterns, for two types of tap changers, the 'Diverter Switch' type and the 'Selector Switch' type OLT changer., Session 1: Network components, DC systems, Energy conversion & Storage

Power transformers are valuable assets in the power network, with the primary function to regulate the transmission and distribution voltage. The regulating device is the tap changer which sets the turn ratio. Tap changer failure is one of the leading causes of high-voltage power transformer failure. Most tap changer failures are caused by degrading contacts. Contact degradation may be the result of contact wear or of arcing-induced carbon deposition during on-load-tap (OLT) changes. Contact degradation increases contact resistance, causing increased heating and arcing, eventually leading to possible failure of the power transformer. Proper maintenance, therefore, requires a diagnostic system which can assess the condition of the contacts. The research presented is aiming at automated defect recognition and localisation from measured dynamic resistance measurement patterns, for two types of tap changers, the 'Diverter Switch' type and the 'Selector Switch' type OLT changer., Session 1: Network components, DC systems, Energy conversion & Storage

In this article the authors endeavor to highlight the constraints facing rural women in the Okara District of Punjab, Pakistan in making decisions pertaining to agriculture and to suggest how to empower rural women to enhance sustainable agriculture development. The study area was selected because women in South Asia participate significantly more in agriculture production than men do, yet because of environmental constraints, a lack of agriculture advice and services and social norms of segregation, women still have little input in the decision making process. Data was compiled using a multistage sampling technique. The first section involved the random selection of three tehsils. In the second section, 8 villages were chosen from each tehsil. In the third section twenty farms were randomly chosen. Using this multistage sampling technique, the researchers were able to randomly select 480 women from the rural Okara District. The research showed a large percentage of women in the rural Okara District were illiterate, destitute, earning less than 1 Lac annually from farming and other domestic activities and live on small farms. A majority of women claimed they experienced difficulty acquiring agricultural extension services due to complex cultural, social, political and economic issues. Female empowerment can be ameliorated by adequately addressing the constraints facing women in agricultural production.

In this article the authors endeavor to highlight the constraints facing rural women in the Okara District of Punjab, Pakistan in making decisions pertaining to agriculture and to suggest how to empower rural women to enhance sustainable agriculture development. The study area was selected because women in South Asia participate significantly more in agriculture production than men do, yet because of environmental constraints, a lack of agriculture advice and services and social norms of segregation, women still have little input in the decision making process. Data was compiled using a multistage sampling technique. The first section involved the random selection of three tehsils. In the second section, 8 villages were chosen from each tehsil. In the third section twenty farms were randomly chosen. Using this multistage sampling technique, the researchers were able to randomly select 480 women from the rural Okara District. The research showed a large percentage of women in the rural Okara District were illiterate, destitute, earning less than 1 Lac annually from farming and other domestic activities and live on small farms. A majority of women claimed they experienced difficulty acquiring agricultural extension services due to complex cultural, social, political and economic issues. Female empowerment can be ameliorated by adequately addressing the constraints facing women in agricultural production.

A novel rotation and scale invariant texture classification methodologyis proposed based on distribution matching in higher dimensionalspace. Feature extraction is performed by using uniform local binarypatterns (uLBPs) in which the rotation and scale changes in animage cause shifts in the underlying uLBP histograms. To compensatefor these shifts at the classification layer, the distributions of trainingand testing data using kernel methods are estimated and means ofthe two distributions in the transformed domain using importanceweights are matched. These calculated importance weights are usedin the standard support vector machines to compensate for the shiftin the distributions. The proposed method is used for classifying theimages in the Brodatz texture database demonstrating the effectivenessof the proposed methodology.