Accurate prediction and optimization of morphological traits in Roselle are essential for enhancing crop productivity and adaptability to diverse environments. In the present study, a machine learning framework was developed using Random Forest and Multi-layer Perceptron algorithms to model and predict key morphological traits, branch number, growth period, boll number, and seed number per plant, based on genotype and planting date. The dataset was generated from a field experiment involving ten Roselle genotypes and five planting dates. Both RF and MLP exhibited robust predictive capabilities; however, RF (R² = 0.84) demonstrated superior performance compared to MLP (R² = 0.80), underscoring its efficacy in capturing the nonlinear genotype-by-environment interactions. Permutation-based feature importance analysis further revealed that planting date had a more significant impact on trait variation than genotype. To identify optimal combinations of genotype and planting date for maximizing morphological traits, the RF model was integrated with the Non-dominated Sorting Genetic Algorithm II (NSGA-II). According to the RF–NSGA-II optimization results, the optimal values, including 26 branches per plant, a growth period of 176 days, 116 bolls per plant, and 1517 seed numbers per plant, were achieved with the Qaleganj genotype planted on May 5. Collectively, these findings highlight the potential of integrating machine learning and evolutionary optimization algorithms as powerful computational tools for crop improvement and agronomic decision-making.
A field experiment was conducted in an agricultural field in Al-Hindia district, Karbala governorate in a silty clay soil during the year 2020. The research included a study of two factors, the first is the depth of plowing at two levels, namely 13 and 20 cm, which represented the main blocks. The second is the tire inflation pressure at two levels, namely (70 and 140 kPa), which represented the secondary blocks. Slippage percentage, field efficiency, leaf area, and 300 grain weight were studied. The experiment was carried out using a split-plot system under a Randomized complete block design, at three replications. The tillage depth of 13 cm exceeds/transcend by giving it the least slippage of (11.01%), the highest field efficiency of (50.
... Show MoreThe aim of this work is to study the histological and histochemical structure of the Harderian gland in indigenous pigeons. Samples were obtained from 10 males and 10 females of adult healthy pigeons. Hematoxylin and eosin, Alcian blue (pH 2.5), periodic acid-Schiff and promo phenol blue, stains were used for paraffin section examination. The gland was teardrop like in shape, light brown to pink in color, capsulated with thin connective tissue. It was multilobular compound acinotubular in structure and lined by columnar epithelial cells. Lymphocyte, plasma cells and plasma cells with Russell bodies were present underneath the epithelia of central collecting duct and around the secretory unite. Histochemically; the
... Show MorePolypyrrole/silver (PPy/Ag) nanocomposites was synthesized via a chemical oxidative method. The AFM analysis is performed to study the surface roughness, morphology and size distribution of the PPy particles and PPy-ag nanocomposites. The results indicated that as the concentration of Ag in the nanocomposite increases, the roughness also increases. The size of nanoparticles was also evaluated and found in the range of 15 nm to 125 nm. The PPy/Ag nanocomposites exhibited an effectiveness against Gram-negative Escherichia coli showing an inhibition zone of 4mm and displayed poor efficacy against Gram-positive Staphylococcus aureus. Based on given adequate antibacterial characteristics of PPy/Ag nanocomposites, it can be identified as a pro
... Show MoreThe solution casting method was used to prepare a polyvinylpyrrolidone (PVP)/Multi-walled carbon nanotubes (MWCNTs) nanocomposite with Graphene (Gr). Field Effect Scanning Electron Microscope (FESEM) and Fourier Transformer Infrared (FTIR) were used to characterize the surface morphology and optical properties of samples. FESEM images revealed a uniform distribution of graphene within the PVP-MWCNT nanocomposite. The FTIR spectra confirmed the nanocomposite information is successful with apperaring the presence of primary distinct peaks belonging to vibration groups that describe the prepared samples.. Furthermore, found that the DC electrical conductivity of the prepared nanocomposites increases with increasing MWCNT concentratio
... Show MorePolypyrrole/silver (PPy/Ag) nanocomposites was synthesized via a chemical oxidative method. The AFM analysis is performed to study the surface roughness, morphology and size distribution of the PPy particles and PPy-ag nanocomposites. The results indicated that as the concentration of Ag in the nanocomposite increases, the roughness also increases. The size of nanoparticles was also evaluated and found in the range of 15 nm to 125 nm. The PPy/Ag nanocomposites exhibited an effectiveness against Gram-negative Escherichia coli showing an inhibition zone of 4mm and displayed poor efficacy against Gram-positive Staphylococcus aureus. Based on given adequate antibacterial characteristics of PPy/Ag nanocomposites, it can be identified as
... Show MoreIn this work, pure and doped Vanadium Pentoxide (V2O5) thin films with different concentration of TiO2 (0, 0.1, 0.3, 0.5) wt were obtained using Pulse laser deposition technique on amorphous glass substrate with thickness of (250)nm. The morphological, UV-Visible and Fourier Transform Infrared Spectroscopy (FT-IR) were studied. TiO2 doping into V2O5 matrix revealed an interesting morphological change from an array of high density pure V2O5 nanorods (~140 nm) to granular structure in TiO2-doped V2O5 thin film .Transform Infrared Spectro
... Show MoreThis study concerned with phytochemical investigation and methods of extraction and separation of active constituents from Valeriana officinalis plant cultivated in Iraq. Due to the large number of active constituents in Valeriana officinalis, it was necessary to make analytical study of its constituents to determine the chemical nature of these constituents and then determine the main classes (terpenes and iridoids) using chemical reagents specific for each class. Different organic solvents like ethanol (70%) used in soxhlet apparatus and hexane, ethyl acetate and methanol were used separately to extract the main active constituents by maceration. Through comparison between these solvents using thin layer chromatograph
... Show More<span>Dust is a common cause of health risks and also a cause of climate change, one of the most threatening problems to humans. In the recent decade, climate change in Iraq, typified by increased droughts and deserts, has generated numerous environmental issues. This study forecasts dust in five central Iraqi districts using machine learning and five regression algorithm supervised learning system framework. It was assessed using an Iraqi meteorological organization and seismology (IMOS) dataset. Simulation results show that the gradient boosting regressor (GBR) has a mean square error of 8.345 and a total accuracy ratio of 91.65%. Moreover, the results show that the decision tree (DT), where the mean square error is 8.965, c
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