Effect of organic and inorganic regimes on growth, production and quality characteristics of cauliflower

Increasing crop yield through balanced use of fertilizers in combination with organic acids is a need of the time to limit the use of costly chemical fertilizers as well as to minimize the environmental pollution in Pakistan.This trial aimed to investigate the benefits of organic and inorganic regimes application on growth, yield and quality of cauliflower. In this study, organic (Farmyard manure (FYM), Poultry manure (PM), Spent mushroom Compost (SMC) at the rate of (15:3:6 t ha-1), inorganic (NPK @ 100:60:60 kg ha1) regimes and Cauliflower cultivars (Kohat local, Hollywood, Lucky, White beauty and Pearl) were used. The organic regime showed highest value for number of leaves plant-1, leaf area, chlorophyll content, plant height, curd diameter, curd weight, curd dry matter content and total yield of the cauliflower. Regarding cultivars, the minimum days to germination, maximum number of leaves plant-1, chlorophyll content, curd diameter, curd weight, curd dry matter content and total yield were recorded in cultivar Lucky. While cultivar White beauty showed maximum leaf area, plant height and curd pH. From the results it is concluded that cultivar Lucky performed well in organic regimes and recommended for better quality and high yield production in Peshawar valley.111

USE OF PROCESSED SUGARCANE BAGASSE ASH IN CONCRETE AS PARTIAL REPLACEMENT OF CEMENT: MECHANICAL AND DURABILITY PROPERTIES

Using biomass waste as supplementary cementing material (SCM) in concrete has attracted researchers’ attention for efficient waste utilization and reducing cement demand. Sugarcane bagasse ash (SCBA) is one such example of biomass waste. It is an agricultural waste obtained when sugarcane bagasse from the sugar industry is used for power generation and disposed of in open-air dumping sites. Its waste disposal causes the generation of particulate matter, degrading air quality. In this study, the effect of processed SCBA as SCM in concrete has been investigated. The processing of the SCBA involved the removal of fibrous and carbon-containing particles by sieving followed by grinding. The SCBA was ground for 45 min until the surface area was comparable to that of cement and was then used for further characterization and incorporation into concrete. The 45 min grinding time resulted in 2.92 times higher pozzolanic reactivity of the SCBA. The SCBA was incorporated by replacing cement in different weight fractions (10%, 20%, 30%, 40%) in concrete. Test results showed that the concrete workability increased with SCBA incorporation, whereas the resulting concrete density was reduced. The results of the mechanical properties, including compressive sstrength and hardened density, were enhanced upon the cement replacement by SCBA. Concrete containing 30% SCBA can be used for structural applications as its 28 days compressive strength was 21 MPa, which complies with ACI 318-16 specifications. Concrete resistance against scaling and leaching due to adverse effects of sulfuric and hydrochloric acid considerably increased with SCBA addition and was due to microstructure densification by secondary hydrates formation as lower portlandite content was detected by thermogravimetric analysis. Hence, SCBA processing increases its reactivity, as reflected by the improved mechanical properties and greater durability of SCBA-incorporated concrete. Keywords: sugarcane bagasse ash; waste disposal; structural concrete; pozzolanic activity; durabilit

Eco-friendly incorporation of crumb rubber and waste bagasse ash in bituminous concrete mix

The consumption of waste materials in the construction sector is a sustainable approach that helps in reducing the environmental pollution and decreases the construction cost. The present research work emphasizes the mechanical properties of bituminous concrete mix prepared with crumb rubber (CR) and waste sugarcane bagasse ash (SCBA). For the preparation of bituminous concrete mix specimens with CR and SCBA, the effective bitumen content was determined using the Marshall Mix design method. A total of 15 bituminous concrete mix specimens with 4%, 4.5%, 5%, 5.5% and 6% of bitumen content were prepared, and the effective bitumen content turned out to be 4.7%. The effect of five different CR samples of 2%, 4%, 6%, 8% and 10% by weight of total mix and SCBA samples of 25%, 50%, 75% and 100% by weight of filler were investigated on the performance of bituminous concrete. A total of 180 samples with different percentages of CR and SCBA were tested for indirect tensile strength (ITS) and Marshall Stability, and the results were compared with conventional bituminous concrete mix. It was observed that the stability values rose with an increase in CR percentage up to 6%, while the flow values rose as the percentage of SCBA increased in the mix. Maximum ITS results were observed at 4% CR and 25% SCBA replacement levels. However, a decrease in stability and ITS result was observed as the percentages of CR and SCBA increased beyond 4% and 25%, respectively. We concluded that the optimum CR and SCBA content of 4% and 25%, respectively, can be effectively used as a sustainable alternative in bituminous concrete mix

Influence of maturity stages on postharvest physico-chemical properties of grapefruit (Citrus paradisi var. ‘Shamber Tarnab’) under different storage durations

The present study was conducted to evaluate the effect of maturity stages on the physicochemical characteristics of grapefruit (Citrus paradisi cv. ‘Shamber Tarnab’) under storage conditions for 60 days at ambient temperature (16±1 °C with 55-60% relative humidity). Grapefruits were harvested at different maturity stages, namely mature green (MG) and full ripe (FR). The fruits of both stages were assessed for different physical quality parameters at 15 days interval. The experimental results showed that ascorbic acid content, titratable acidity, fruit firmness, percent disease incidence was higher at FR stage. In contrast, weight loss, percent juice content, total soluble solid (TSS), and TSS/acid ratio at MG (mature green) were lower than that of FR fruits. Regarding storage durations, the fruit firmness, titratable acidity, percent juice content, ascorbic acid content decreased significantly, whilst total soluble solid, TSS/Acid ratio, weight loss, and percent disease incidence increased significantly with the extension of storage duration from 0 to 60 days. As concerned to its interactive effects, the highest ascorbic acid content, titratable acidity, percent juice content, and maximum fruit firmness were observed in fresh grapefruit, harvested at (MG) mature green stages, whereas the maximum total soluble solid, percent disease incidence, and TSS/Acid ratio were recorded in fruit harvested at (FR) full ripe stage, stored for 60 days at room temperature. Similarly, the Pearson’s Correlation Analysis (p> 0.05) of grapefruit was positive effect for most of the quality traits of grapefruit at different storage durations and maturity stages. It was concluded that grapefruit could be harvested at the mature green stage (MG) for sustaining quality attributes up to 60 days of storage at room temperature

Development of a Hydrodynamic-Based Flood-Risk Management Tool for Assessing Redistribution of Expected Annual Damages in a Floodplain

Despite spending ample resources and procedural development in flood management, flood losses are still increasing worldwide. The losses caused by floods and costs incurred on management are two components of expected annual damages (EAD) due to floods. This study introduces a generalized approach for risk-based design where a range of probable floods are considered before and after a flood mitigation measure is implemented. The proposed approach is customized from the ISO Guide 31000 along with additional advantages of flood risk visualization. A Geographic Information System (GIS)-based design of a flood-protection dike is performed to exhibit the risk redistribution. The Chenab River is selected for the existing dike system. Detailed hazard behaviour and societal vulnerability are modelled and visualized for a range of all probable floods before and after the implementation of flood-protection dikes. EAD maps demonstrate the redistribution of induced and residual risks. It can be concluded that GIS-based EAD maps not only facilitate cost-effective solutions but also provide an accurate estimate of residual risks after the mitigation measures are applied. EAD maps also indicate the high-risk areas to facilitate designing secondary measures

A hydraulic analysis of shock wave generation mechanism on flat spillway chutes through physical modeling

Shock waves are generated downstream of spillways during flood operations, which have adverse effects on spillway operations. This paper presents the physical model study of shock waves at the Mohmand Dam Spillway project, Pakistan. In this study, hydraulic analysis of shock waves was carried out to investigate its generation mechanism. Different experiments were performed to analyze the rooster tail on a flat spillway chute and to examine the factors affecting the characteristics of the rooster tail. The study results show that shock wave height is influenced by spillway chute slope, pier shape, and flow depth. Moreover, the height of the shock wave can be minimized by installing a semi-elliptical pier on the tail part of the main pier. Further modifications in the geometry of the extended tail part of the pier are recommended for the elimination of the shock wave. Based on observed data collected from the model study, an empirical equation was developed to estimate the shock wave height generated on the flat slope spillway chutes (5◦ to 10◦ )

Comparative Assessment of Individual and Ensemble Machine Learning Models for Efficient Analysis of River Water Quality

The prediction accuracies of machine learning (ML) models may not only be dependent on the input parameters and training dataset, but also on whether an ensemble or individual learning model is selected. The present study is based on the comparison of individual supervised ML models, such as gene expression programming (GEP) and artificial neural network (ANN), with that of an ensemble learning model, i.e., random forest (RF), for predicting river water salinity in terms of electrical conductivity (EC) and dissolved solids (TDS) in the Upper Indus River basin, Pakistan. The projected models were trained and tested by using a dataset of seven input parameters chosen on the basis of significant correlation. Optimization of the ensemble RF model was achieved by producing 20 sub-models in order to choose the accurate one. The goodness-of-fit of the models was assessed through well-known statistical indicators, such as the coefficient of determination (R2), mean absolute error (MAE), root mean squared error (RMSE), and Nash–Sutcliffe efficiency (NSE). The results demonstrated a strong association between inputs and modeling outputs, where R2 value was found to be 0.96, 0.98, and 0.92 for the GEP, RF, and ANN models, respectively. The comparative performance of the proposed methods showed the relative superiority of the RF compared to GEP and ANN. Among the 20 RF sub-models, the most accurate model yielded the R2 equal to 0.941 and 0.938, with 70 and 160 numbers of corresponding estimators. The lowest RMSE values of 1.37 and 3.1 were yielded by the ensemble RF model on training and testing data, respectively. The results of the sensitivity analysis demonstrated that HCO3− is the most effective variable followed by Cl− and SO42− for both the EC and TDS. The assessment of the models on external criteria ensured the generalized results of all the aforementioned techniques. Conclusively, the outcome of the present research indicated that the RF model with selected key parameters could be prioritized for water quality assessment and management

Comparative Assessment of Individual and Ensemble Machine Learning Models for Efficient Analysis of River Water Quality

The prediction accuracies of machine learning (ML) models may not only be dependent on the input parameters and training dataset, but also on whether an ensemble or individual learning model is selected. The present study is based on the comparison of individual supervised ML models, such as gene expression programming (GEP) and artificial neural network (ANN), with that of an ensemble learning model, i.e., random forest (RF), for predicting river water salinity in terms of electrical conductivity (EC) and dissolved solids (TDS) in the Upper Indus River basin, Pakistan. The projected models were trained and tested by using a dataset of seven input parameters chosen on the basis of significant correlation. Optimization of the ensemble RF model was achieved by producing 20 sub-models in order to choose the accurate one. The goodness-of-fit of the models was assessed through well-known statistical indicators, such as the coefficient of determination (R2), mean absolute error (MAE), root mean squared error (RMSE), and Nash–Sutcliffe efficiency (NSE). The results demonstrated a strong association between inputs and modeling outputs, where R2 value was found to be 0.96, 0.98, and 0.92 for the GEP, RF, and ANN models, respectively. The comparative performance of the proposed methods showed the relative superiority of the RF compared to GEP and ANN. Among the 20 RF sub-models, the most accurate model yielded the R2 equal to 0.941 and 0.938, with 70 and 160 numbers of corresponding estimators. The lowest RMSE values of 1.37 and 3.1 were yielded by the ensemble RF model on training and testing data, respectively. The results of the sensitivity analysis demonstrated that HCO3− is the most effective variable followed by Cl− and SO42− for both the EC and TDS. The assessment of the models on external criteria ensured the generalized results of all the aforementioned techniques. Conclusively, the outcome of the present research indicated that the RF model with selected key parameters could be prioritized for water quality assessment and management

Performance Evaluation of Soft Computing for Modeling the Strength Properties of Waste Substitute Green Concrete

The waste disposal crisis and development of various types of concrete simulated by the construction industry has encouraged further research to safely utilize the wastes and develop accurate predictive models for estimation of concrete properties. In the present study, sugarcane bagasse ash (SCBA), a by-product from the agricultural industry, was processed and used in the production of green concrete. An advanced variant of machine learning, i.e., multi expression programming (MEP), was then used to develop predictive models for modeling the mechanical properties of SCBA substitute concrete. The most significant parameters, i.e., water-to-cement ratio, SCBA replacement percentage, amount of cement, and quantity of coarse and fine aggregate, were used as modeling inputs. The MEP models were developed and trained by the data acquired from the literature; furthermore, the modeling outcome was validated through laboratory obtained results. The accuracy of the models was then assessed by statistical criteria. The results revealed a good approximation capacity of the trained MEP models with correlation coefficient above 0.9 and root means squared error (RMSE) value below 3.5 MPa. The results of cross-validation confirmed a generalized outcome and the resolved modeling overfitting. The parametric study has reflected the effect of inputs in the modeling process. Hence, the MEP-based modeling followed by validation with laboratory results, cross-validation, and parametric study could be an effective approach for accurate modeling of the concrete properties.publishedVersio

Fuzzy Logic Based Edge Detection in Smooth and Noisy Clinical Images.

Edge detection has beneficial applications in the fields such as machine vision, pattern recognition and biomedical imaging etc. Edge detection highlights high frequency components in the image. Edge detection is a challenging task. It becomes more arduous when it comes to noisy images. This study focuses on fuzzy logic based edge detection in smooth and noisy clinical images. The proposed method (in noisy images) employs a 3 × 3 mask guided by fuzzy rule set. Moreover, in case of smooth clinical images, an extra mask of contrast adjustment is integrated with edge detection mask to intensify the smooth images. The developed method was tested on noise-free, smooth and noisy images. The results were compared with other established edge detection techniques like Sobel, Prewitt, Laplacian of Gaussian (LOG), Roberts and Canny. When the developed edge detection technique was applied to a smooth clinical image of size 270 × 290 pixels having 24 dB 'salt and pepper' noise, it detected very few (22) false edge pixels, compared to Sobel (1931), Prewitt (2741), LOG (3102), Roberts (1451) and Canny (1045) false edge pixels. Therefore it is evident that the developed method offers improved solution to the edge detection problem in smooth and noisy clinical images