The utilization of sugarcane molasses (SCM), a byproduct of sugar refining, offers a promising bio-based alternative to conventional chemical admixtures in cementitious systems. This study investigates the effects of SCM at five dosage levels, 0.25%, 0.50%, 0.75%, 1.00%, and 1.25% by weight of cement, on cement mortar performance across fresh, mechanical, thermal, durability, and density criteria. A comprehensive experimental methodology was employed, including flow table testing, compressive strength (7, 14, and 28 days) and flexural strength measurements, embedded thermal sensors for real-time hydration monitoring, water absorption and chloride ion penetration tests, as well as 28-day density determination. Results revealed clear dose-dependent behavior, with SCM enhancing mortar flowability proportional to dosage, raising the spread diameter from 11.5 cm (control) to 20 cm at 1.25%. At 0.25% SCM, compressive strength (47.5 MPa at 28 days) and flexural strength (~2.9 MPa) were higher than those of the remaining SCM dosages, supported by sustained heat release and positive temperature differentials. However, dosages ≥ 0.5% drastically suppressed hydration kinetics and mechanical performance, with compressive strength falling below 10 MPa. Furthermore, high SCM content led to increased water absorption (up to 10.6%) and chloride permeability (CIP above 5100 C), while bulk density declined from 2250 kg/m3 to 2080 kg/m3 at 1.25% SCM. Statistical validation using one-way ANOVA confirmed that these differences across dosage levels were significant (p < 0.05), underscoring the importance of dosage optimization. This investigation confirms that low-dosage SCM (≤0.25%) can be an effective bio-additive, providing improved workability with negligible compromise in strength and durability. In contrast, higher dosages undermine matrix integrity and performance. Future work is recommended to assess long-term microstructural evolution, field exposure durability, and adaptability across diverse cementitious systems.
The precise classification of DNA sequences is pivotal in genomics, holding significant implications for personalized medicine. The stakes are particularly high when classifying key genetic markers such as BRAC, related to breast cancer susceptibility; BRAF, associated with various malignancies; and KRAS, a recognized oncogene. Conventional machine learning techniques often necessitate intricate feature engineering and may not capture the full spectrum of sequence dependencies. To ameliorate these limitations, this study employs an adapted UNet architecture, originally designed for biomedical image segmentation, to classify DNA sequences.The attention mechanism was also tested LONG WITH u-Net architecture to precisely classify DNA sequences
... Show MoreModern statistical techniques offer a range of methodologies for modelling time series data, with conditional and unconditional approaches providing complementary insights that enhance overall model accuracy. This article introduced a modified ARIMA model employing conditional and unconditional parameter estimates. The methodology for the new model based on novel methods is provided. The prediction process, one and two steps ahead, is covered in detail, and a novel algorithm is presented. The best model is picked based on various measurement criteria, such as coefficient of determination (R2), root mean squared error (RMSE), and mean absolute scaled error (MASE). The suggested model is applied to a monthly petrol sales dataset (Jan
... Show MoreThis study utilized low-cost agricultural waste (molasses production waste powder) to extract copper ions from aqueous solutions. The present investigation explored a range of factors that influence the adsorption process, including temperature, pH, ionic strength, contact time, quantity of adsorbent, and particle size. Spectrophotometric analysis was used to determine the solution's absorbance both before and after the adsorption procedure. The Langmuir and Freundlich adsorption models were used to match the equilibrium data. The Freundlich model was determined to be the best isotherm model using the linear regression coefficient R2=0.9868. Thermodynamic parameters, including enthalpy, entropy, and Gibbs free energy, were calculate
... Show MoreAbstract:
This research seeks to test the influence of intellectual capital as an explanatory variable and its components (human capital, structural capital, relational capital) and sustainable competitive performance as a responsive variable and its components (reducing service delivery cycle time, rapid response to market demand, increasing customer satisfaction, providing better Quality of service, increasing market share)” through a field study, and here the research problem was diagnosed in an attempt to answer the following question: Is there awareness among the senior management within the private colle
... Show MoreThe substantial key to initiate an explicit statistical formula for a physically specified continua is to consider a derivative expression, in order to identify the definitive configuration of the continua itself. Moreover, this statistical formula is to reflect the whole distribution of the formula of which the considered continua is the most likely to be dependent. However, a somewhat mathematically and physically tedious path to arrive at the required statistical formula is needed. The procedure in the present research is to establish, modify, and implement an optimized amalgamation between Airy stress function for elastically-deformed media and the multi-canonical joint probability density functions for multivariate distribution complet
... Show MoreIraq faces persistent challenges in achieving sustainable development due to decades of conflict, political instability, and infrastructural degradation. These challenges are particularly evident in critical sectors such as energy, water, healthcare, education, and governance, which significantly influence human well-being, social equity, and quality of life. This study proposes an AI-driven, ethically guided, and human-centric sustainability framework to support resilient urban transformation in Iraq.
New designs of solar using ray tracing program, have been presented for improved the performance and the out put power of the silicon solar cell, as well as reducing the cost of system working by solar energy. Two dimensional solar concentrator (Fresnel lenses) and three dimensional concentrators (parabola dish and cassegrain) were used as concentrator for photovoltaic applications (CPV). The results show that the performance efficiency and out power for crystalline silicon solar cells are improved.