The University of Engineering and Technology, Peshawar (UET Peshawar), formerly known as NWFP University of Engineering and Technology, is a public university located in Peshawar, Khyber Pakhtunkhwa, Pakistan. Formerly known as NWFP University of Engineering and Technology until 2010, it an institution of higher learning in Pakistan with a strong emphasis on the development of science and engineering.Its programs of civil engineering includes research in the interdiscipline of earthquake studies, started after the massive earthquake jolted the country in 2005. The University offers undergraduate and post-graduate programs in various engineering disciplines. The University is also a member of the Association of Commonwealth Universities of the United Kingdom. The University is regarded as one of the best engineering schools in the country and is ranked in 7th position nationwide in the category of "Engineering and Technology" by HEC, the highest governing body of higher education in Pakistan. In addition, the University has been retaining its position among the top institutions of science and engineering in the country for a long time.
Graphene and iron oxide nanocomposite materials attracted significant attention in different disciplines including optoelectronics, catalysis, and energy conversion/storage devices. Despite the extreme potential, a major obstacle had been the lack of effective and environmentally benign production techniques for mass-producing iron oxide-graphene nanocomposites. To overcome the obstacle, we opted for an efficient, facile, and eco-friendly hydrothermal synthesis route for the synthesis of iron oxide-graphene nanocomposites. The technique involved the homogenous mixing of metal salt precursor (iron chloride), and graphene oxide (GO) followed by a hydrothermal reaction under normal conditions. The synthesized nanocomposites were systematically investigated for structural, morphological, thermal, optical, and magnetic characteristics using XRD, Raman, SEM, TGA, UV–Vis, PL, and VSM techniques. The XRD and Raman studies confirmed the formation of α-Fe2O3-RGO and Fe3O4-RGO nanocomposites. The SEM images disclosed the anchoring of metal oxide nanoparticles to graphene nanosheets. The nanocomposite exhibited enhanced thermal stability compared to the pristine GO sample. The optical studies corroborated the better charge transfer response of nanocomposites and Hall effect measurements affirmed these nanocomposites as charge transport materials. The VSM measurements confirmed the magnetic behavior of the samples. Therefore, these nanocomposite materials could be a viable option for optoelectronics and energy conversion/storage devices.
Metal-organic frameworks (MOFs) have emerged as promising materials for CO2 capture, owing to their high surface areas and diverse chemical functionalities. However, their practical application is often hindered by stability concerns, particularly in water-rich environments and under fluctuating thermal and chemical conditions. This paper provides a state-of-the-art review of MOFs, focusing on their water, thermodynamic, kinetic and chemical stabilities and functions. Additionally, comparative analysis of MOF stability types and key design rules for their stability enhancement are discussed. Furthermore, role of artificial intelligence (AI) and machine learning (ML) in revolutionizing MOF research for their optimized formation (discovery, screening, identification, design, synthesis) and prediction of CO2 capture capacity has been reviewed. Moreover, this paper outlines the challenges and limitations like lack of datasets, feature interpretability, model transferability, scalability and practical applicability. The findings revealed that High-throughput screening, AI-ML-driven optimized design and synthesis of Aluminum Porphyrin-MOF (Al-PMOF) which produced with over 80% yield in only 50 min instead of 16-h traditional method, making it 20 times faster than conventional approaches, while examining 320,000 hMOFs database. Additionally, AI-ML driven, Least Squares Support Vector Machine- genetic optimization (LSSVM-GO) a hybrid ML model has shown prediction accuracy up to R-square value of 0.9797 for CO2 adsorption capacity of MOFs. In conclusion, AI-ML driven frameworks significantly outperform conventional approaches in the design, discovery, synthesis and prediction of CO2 adsorption in MOFs. Looking ahead, the integration AI-ML models with autonomous synthesis by utilizing robotic/additive manufacturing to reduce costs, time and expedite the creation of innovative MOFs.
Recycled aggregate concrete (RAC) offers sustainability advantages but often shows reduced strength and durability compared with normal aggregate concrete (NAC). This review evaluates the role of basalt fibers (BF) in improving the performance of both NAC and RAC, drawing on over 150 experimental studies. Results show that properly selected BF dosages can enhance compressive, splitting tensile and flexural strengths by up to about 25%, 35%, and 60%, respectively. Most effective mixtures use 0.1-0.5% BF by volume with fiber lengths of 6-18 mm. In RAC, BF performs best at moderate recycled aggregate replacement levels of 40-50%, where it can significantly recover strength losses and, in several cases, achieve compressive strengths above 55 MPa. At these levels, BF also refines pore structure and improves resistance to freeze - thaw damage and chloride penetration. However, excessive dosages (>= 0.6%) frequently reduce workability and promote fiber clumping and higher porosity. Overall, the findings show that optimized BF content and geometry, together with appropriate RAC mix design and aggregate treatment, can yield more durable and sustainable concrete, while underscoring the need for further research on hybrid fiber systems and performance prediction models for BFRC. (sic)(sic)(sic)(sic)(sic)(sic)(sic) (RAC)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic),(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic) (NAC)(sic)(sic),(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic). (sic)(sic)(sic)(sic)(sic)150(sic)(sic)(sic)(sic)(sic)(sic),(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic) (BF)(sic)(sic)(sic)NAC(sic)RAC(sic)(sic)(sic)(sic)(sic)(sic)(sic). (sic)(sic)(sic)(sic),(sic)(sic)(sic)(sic)(sic)BF(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic),(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)25%,35%(sic)60%. (sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)0.1-0.5%(sic)(sic)(sic)BF,(sic)(sic)(sic)(sic)(sic)6-18 mm. (sic)RAC(sic),BF(sic)40-50%(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic),(sic)(sic)(sic)(sic)(sic)(sic),(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic),(sic)(sic)(sic)(sic)(sic)(sic),(sic)(sic)(sic)(sic)55 MPa(sic)(sic)(sic)(sic)(sic)(sic)(sic). (sic)(sic)(sic)(sic)(sic)(sic),(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic),(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic). (sic)(sic),(sic)(sic) (>= 0.6%)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic),(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic). (sic)(sic)(sic)(sic),(sic)(sic)(sic)(sic)(sic)(sic),(sic)(sic)(sic)BF(sic)(sic)(sic)(sic)(sic)(sic)(sic),(sic)(sic)(sic)(sic)(sic)RAC(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic),(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic),(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)BFRC(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic).
Long-haul optical transmission (LHOT) systems are affected by nonlinear impairments (NIs), including self-phase modulation (SPM), cross-phase modulation (XPM), four-wave mixing (FWM), amplified spontaneous emission (ASE) noise, and Kerr nonlinearities, which limit achievable data rates and system reach. Conventional methods, such as digital back-propagation (DBP), optical phase conjugation (OPC), and DSP-assisted receivers, have demonstrated mitigation capabilities but suffer from high computational complexity, latency, and power consumption, making them impractical for large-scale networks. Machine learning (ML) approaches, including label propagation and transformer-based schemes, reduce some processing overhead yet do not perform dimensionality reduction for feature compression and lack a mechanism to jointly handle multiple nonlinear effects across LHOT. Furthermore, most reported works do not align with optical communication standards, such as ITU-T G.652.D or OS1/OS2 fibers, which limits their practical implementation in standardized infrastructures. This work proposes an autoencoder-based pelican optimization algorithm (APOA) for NIs mitigation in LHOT systems. The autoencoder compresses high-dimensional signal distortions into a latent space that preserves nonlinear mappings, reducing computational load while maintaining representation accuracy. The POA performs parameter tuning to optimize signal recovery in the presence of nonlinear effects and noise. The transmission channel is modeled using the nonlinear Schrodinger equation (NLSE), with propagation distortions characterized by ITU-T G.652.D single-mode fiber (SMF) parameters: attenuation of 0.20 dB/km, chromatic dispersion of similar to 17 ps/nm/km at 1550 nm, effective area of 80 mu m(2), and nonlinear coefficient gamma approximate to 1.3 W(-1)km(-1). Simulations are conducted using parameter settings aligned with OS1/OS2 fiber specifications (9 mu m core diameter) and representative optical communication terminal (OCT) configurations, to reflect realistic long-haul transmission environments. Performance evaluation across multiple OSNR levels, fiber lengths, and modulation formats uses FEC thresholds and operating ranges that are consistent with IEEE 802.3 Ethernet and ITU-T G.709 OTN reference values, showing that APOA achieves BER values below the adopted FEC thresholds, increases spectral efficiency, and extends transmission reach.
In modern manufacturing, machine learning (ML) plays a crucial role in predicting response measures, reducing machining trial costs and saving energy. This study investigates surface evolution and cutting forces (Favg) during the micro-milling of selective laser-melted (SLM) Ti6Al4V, focusing on spindle speed (SS), depth of cut (DOC), and feed rate (FZ). Analysis of variance (ANOVA) identified FZ as the most influential on surface roughness (Ra) of about 38.54
