Kerbala Journal for Engineering Sciences

The Architectural Intent is a spoken language without any sounds; it extends the forms, lines, and even materials. The designer uses his intent to express his own design view, which is not related to the function or elements. From this point of view, the Architectural Intent is used as a tool to analyse and understand contemporary Architecture and its identity renewal types. This study aimed to explore the effect of the Architectural intent types (the individual, the collective, and the hybrid) on the present Iraqi Architecture Identity formation, by using four central dimensions; The Design Vision, the Architectural Purpose, the stylistic affiliation, and the position of heritage. This study used the applied analytical Methodology which is the most effective Methodology to explore the study goals with a subjective approach. By four local case studies of the present most outstanding Iraqi Design Projects: Iraqi Central Bank tower (ICB), Bismayah City, Bodor Al-Najaf, and General Secretariat of the Iraqi Council of Ministers building (GSICM) projects. The main outcomes of this Study, the Hybrid type of the architectural intent had the common characteristics for both other types, therefore it refers to the balanced route to produce a present local identity architecture. The main recommendations were to strength the hybrid intent by using it as a strategic connectivity tool between Man and his built-up environment.

Cold asphalt mixtures (CAM) have many economic and environmental benefits compared to traditional hot mix asphalt (HMA). However, because such a CAM has weak strength and high air voids, it receives little attention. The main objective of this research is to enhance traditional CAM by integrating two types of biomass ash: palm leaf ash (PLA) and reed ash (RA). The improvement in the mechanical properties of CMA was accomplished by replacing ordinary Portland cement (OPC) with PLA at proportions of 0%, 1.75%, 3.5%, 5.25%, and 7% by dry aggregate weight. RA was added later at concentrations of 0.25%, 0.5%, and 1% based on the dry aggregate weight. The mechanical characteristics and durability were evaluated by the Marshall stability, indirect tensile stress test, and water sensitivity test. The results indicated that 1.5% PLA, 5.5% OPC and 0.45% RA significantly enhanced the Marshall stability, where 15.9 kN were obtained after 3 days and 21.1 kN after 28 days, and this is due to the combined action of both PLA and RA, in addition to OPC activating the production of hydration products.

One of the main causes of cancer-related mortality among women globally is breast cancer. It presents a significant barrier to community public health, and better patient retrieval and intervention depend on early diagnosis and careful planning. Since breast cancer is one of the most significant or exclusive predictors of survival, machine learning and deep learning techniques have a great potential to increase the accuracy of both classification and identification. With a focus on visual data (mammograms) and molecular data (genomic biomarkers), this study offers a thorough summary of current developments in early breast cancer detection strategies. Strong techniques for learning from diverse data sources are provided by conventional deep learning models. However, its scalability, interpretability, and efficiency are dubious when applied to large-dimensional biological datasets. It has been suggested that topical research on explainable AI could improve the precision, openness, and dependability of early breast cancer diagnosis. By bypassing the black-box aspect of traditional AI systems, these technologies are intended to improve the slide and clinical credibility of figures on deep learning models. In order to speed up data analysis and improve prediction skills in healthcare imaging, researchers have also investigated well-established machine learning techniques. The development of AI-driven diagnostic tools that are more user-friendly and appropriate for practical medical applications is made possible by this advancement.

The study makes an important contribution to the research gap that has existed in efforts to maximize energy efficiency and thermal comfort in the indoor environment since it presents an unsteady numerical analysis of the influence of the height of the cooling device on the temperature distribution in an enclosed space. The study employs ANSYS FLUENT 16 to evaluate the influence of the location of the devices on the air velocity, and heat distribution. The findings indicate that a height of 2.4 m for the cooling device is more effective than the other configurations in enhancing the distribution of temperature, and air velocity. It is found that the increase in the height device to 2.4 m at a speed of 2.5 m/s results in an improvement in heat transfer of 0.377% at Point 1 and 0.288% at Point 3. Similarly, the increase in heat transfer at Point 1 and Point 3 at 6.5 m/s is 0.227 % and 0.184 %, respectively. These results prove that height is more influential on the efficiency of heat transfer compared to air conditioning. This paper highlights the importance of the strategic location of cooling systems to enhance their effective use and thermal comfort, thus making contribution towards the sustainable design and operation of structures.

The present work presents the dynamic modeling and nonlinear control of a 5-DOF artificial human arm, which can efficiently replicate human upper limb movements. A control-oriented 5-DOF rigid-body dynamic model was formulated by employing the Euler-Lagrange equation, which considers inertia, centrifugal, and gravitational forces. A total of four control approaches, namely Proportional-Derivative Control (PD), Computed Torque Control (CTC), Linear Quadratic Regulator (LQR), and Adaptive Sliding Mode Control (ASMC), were proposed and analyzed for their trajectory tracking abilities. Lyapunov stability theory was used to validate the convergence of the ASMC and CTC control approaches, whereas the optimal control gains were calculated for the LQR control approach by linearizing the state-space model. The simulation outcomes demonstrated that the ASMC control approach provides the highest level of trajectory tracking accuracy with an RMS error of 0.012 rad, compared to other control approaches such as CTC (0.041 rad), LQR (0.056 rad), and PD control (0.084 rad). The ASMC control strategy was found to have the fastest settling time of 0.78 s and the smallest overshoot of 2.1%, which confirms its robustness against parametric uncertainties. However, it is achieved at the expense of an increased control effort, with an average torque norm of 15.5 Nm, whereas the PD method requires the least control energy (6.2 Nm) but has poor accuracy. These results have demonstrated the effectiveness of nonlinear robust control, especially ASMC, in improving the tracking capability of bio-inspired robotic manipulators.