Dawood University of Engineering & Technology

The current study explores the synthesis, structural characterization, and pharmacological assessment of 9,9-dibutylfluorene-2-carboxylic acid, focusing on its potential as an inhibitor of dihydrofolate reductase (DHFR), a crucial enzyme in cancer treatment. SC-XRD confirmed its molecular structure, detailing essential bond lengths and angles, while Hirshfeld surface analysis identified significant intermolecular interactions primarily driven by H-bonding and van der Waals forces. Density Functional Theory (DFT) revealed stable electronic properties, providing deeper insight into the optimized geometric parameters. Molecular docking established a strong binding affinity to DHFR, indicating promising inhibitory effects. Furthermore, pharmacokinetic analysis suggested favorable drug-like properties, including high gastrointestinal absorption. Together, the findings present this compound as a fascinating candidate for future development as a fluorene-based anticancer agent.

Hydraulic fracturing fractures hydrocarbon reservoirs with fracturing fluids. These fractures facilitate the transportation of proppants in the formation, yet literature have shown that the fracturing fluid with traditional composition often leaves undesirable residues.This clogs reservoir formations, reducing water flow across hydrocarbon reservoirs. The invention of an inexpensive, environmentally friendly fracturing fluid is pushing hydraulic fracturing technology progress. VES fluid is one of the most often used hydraulic fracturing fluids for unconventional reservoir development. Because it leaves no residue after gel-breaking processing. This behaviour of residue-free after a gel breaking ensures minimal formation damage compared with the polymer0based conventional fluids. However, classic single-chain VES fluid has limited shear and temperature resistance. Two modified VES fluids were used as thickening fracturing fluids after this experiment. By joining extra single chains with hydrotropes, or ionic organic salts, non-covalent interaction was achieved. This innovative product is made by combining the long-chain cationic surfactant cetyltrimethylammonium bromide (CTAB) with organic acids citric acid (CA) and maleic acid (MA) at molar ratios of two to one and three to one, respectively. CTAB and CA create a VES-fluid with excellent fracture properties. Empirical study has shown these CTAB-based VES-fluid properties. The settling velocity of the proppant in the fluid was observed to assess the sand-carrying suspension's capacity. Core-flooding tests were used for this research. After adding ethanol, the viscosity of fractured VESfluid was measured to determine its gel breaking ability. The investigations show that the 30 (mM) VES-fluid, also known as CTAB-CA, has the highest Visco-elasticity at pH 6.17. At zero shear rate, the fluid's apparent viscosity was 106 mPa & sdot;s. The apparent viscosity of 30 (mM) CTAB-CA VES-fluid remains at 60 (m Pa & sdot;s) at 90 degrees C and 170 (s - 1) after 2 h of shearing, indicating exceptional resistance to temperature and shear. At 90 degrees Celsius, the CTAB-CA VES-fluid at 30 mM displayed exceptional sand suspension and gel breaking properties. This was determined by researchers. After adding 10 to 1 ethanol, the gel broke within two hours. Gel breaking was completed at 1.67 mm/s sand suspension velocity. According to core flooding investigations, the CTAB-CA VES-fluid creates 7.99% core damage, which is not substantial. It is suggested through an experimental investigation that the CTAB-CA VES-fluid, when heated to high temperatures, will generate the novel thickening fracturing fluid.

Post-industrial land transformations in rapidly urbanizing cities are increasingly incorporating green infrastructure (GI) to address environmental degradation and improve urban liveability. However, there remains a limited understanding of how such ecological interventions shape user perception, belonging, and everyday social experience in the Global South. This study examines the adaptive reuse of a former industrial site in Naya Nazimabad, Karachi, to evaluate how GI influences residents’ perceived environmental quality, comfort, satisfaction, and sense of place. Temporal mapping of satellite imagery (2001–2024) was combined with a structured survey (N = 141) and statistical analysis, including Principal Component Analysis, Kruskal–Wallis tests, ordinal logistic regression, and mediation modelling. Results indicate that increases in tree cover, shaded walkways, parks, and stormwater-sensitive landscapes are associated with improved perceptions of air quality, noise conditions, and outdoor comfort. Frequent engagement with green spaces enhances satisfaction, which in turn mediates feelings of belonging and place attachment. Awareness of the site’s industrial history further strengthens identity and emotional connection to the neighbourhood. Ordinal logistic regression shows that comparative environmental quality is the strongest determinant of higher belonging categories (β = 1.406, SE = 0.282, z = 4.989, p < 0.001; OR ≈ 4.08), with satisfaction (p ≈ 0.09) and tenure (p ≈ 0.06) positive but marginal; mediation modelling confirms a significant indirect effect of usage on belonging via satisfaction (a × b = 0.072; 95% CI [0.013, 0.150]), evidencing a behavioural-to-affective pathway from routine engagement to place attachment. Findings indicate that enhancing comparative environmental quality and everyday GI satisfaction via shade, parks, and drainage can strengthen belonging and place attachment in post-industrial regeneration.

To overcome vendor lock-in and reliability issues in single-cloud deployments, organizations increasingly adopt multi-cloud environments. However, scheduling Spark workflows across heterogeneous clouds under simultaneous deadline and budget constraints remains challenging due to resource diversity, variable pricing, and cross-cloud data transfers. We propose the Deadline Budget Spark Workflow Scheduling to Multi-Cloud (DB-SWSMC) algorithm, a novel scheduling algorithm combining heuristic initialization with simulated annealing optimization to: (1) efficiently allocate resources while balancing cost-time tradeoffs, (2) handle intra/inter-cloud data dependencies, and (3) rigorously enforce constraints. Evaluations across five workflows and compared against existing algorithms (HBDCWS, DBCS, and BDHEFT). Experimental results demonstrate that DB-SWSMC outperforms existing algorithms by 20-40% in cost efficiency and 15-80% in success rates, especially under tight budget and deadline constraints.

The detection of lung diseases, especially cancer, is a complex task with more complexities and features. Many Artificial Intelligence (AI) and machine learning (ML) technologies are leveraged to address lung cancer, an area that remains complex and open to further research. Whereas human testing tools still make many errors in understanding lung disease in humans. In this paper, we present hybrid techniques, including deep reinforcement learning and a convolutional neural network (CNN), for lung cancer detection using single- and linear X-ray datasets from clinical practice. Our objective is to improve lung cancer detection accuracy using trial-and-error deep Q-learning and to train the model on features extracted from CNN. We tested the hybrid model at the local laboratory using 10,000 lung cancer images and trained it using adaptive, dynamic weights with CNN and DQN techniques. Simulation results show that hybrid methods achieve higher accuracy than existing methods for more complex parameter settings.