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.
Abstract
Much attention has been paid for the use of robot arm in various applications. Therefore, the optimal path finding has a significant role to upgrade and guide the arm movement. The essential function of path planning is to create a path that satisfies the aims of motion including, averting obstacles collision, reducing time interval, decreasing the path traveling cost and satisfying the kinematics constraints. In this paper, the free Cartesian space map of 2-DOF arm is constructed to attain the joints variable at each point without collision. The D*algorithm and Euclidean distance are applied to obtain the exact and estimated distances to the goal respectively. The modified Particle Swarm Optimization al
... Show MorePurpose:To evaluate knowledge, practice and attitude of community pharmacists in Basra regarding modified release dosage forms which are widely used for many therapeutic purposes in pharmacy practice.
Methods:The current study was conducted among certified pharmacists in Basra governorate- south of Iraq. Data collection was carried out by a questionnaire.
Results:A total number of 175 community pharmacists responded to the questionnaire. The majority worked in OTC based dispensing pharmacies located in the center of the city. Most respondents missed K1 and were unable to state the difference between different types of modified products. There was a major positive agreemen
... Show MoreThis paper presents a modified training method for Recurrent Neural Networks. This method depends on the Non linear Auto Regressive (NARX) model with Modified Wavelet Function as activation function (MSLOG) in the hidden layer. The modified model is known as Modified Recurrent Neural (MRN). It is used for identification Forward dynamics of four Degrees of Freedom (4-DOF) Selective Compliance Assembly Robot Arm (SCARA) manipulator robot. This model is also used in the design of Direct Inverse Control (DIC). This method is compared with Recurrent Neural Networks that used Sigmoid activation function (RS) in the hidden layer and Recurrent Neural Networks with Wavelet activation function (RW). Simulation results shows that the MRN model is bett
... Show MoreHigh performance work systems and general industrial enterprise performance