Sol–gel synthesis was used to prepare M-type strontium hexaferrite (SrFe12O19) nanoparticles, which were thoroughly analyzed through both first-principles calculations and experimental characterization techniques. The phase formation and structural development were investigated using X-ray powder diffraction along with Rietveld refinement, confirming the formation of a highly crystalline single-phase hexagonal magnetoplumbite structure with space group P63/mmc. No impurity phases were detected, while systematic peak broadening was observed without any noticeable peak shifting. The lattice parameters a and c were found to of 5.776 Å and 22.054Å, respectively. Dielectric constant and AC electrical resistivity exhibited enhanced values, and dielectric measurements revealed relaxation behavior at higher frequencies. Density functional theory calculations within the GGA- PBE framework indicate a semiconducting nature with an indirect band gap of approximately 1.81 eV, dominated by Fe-3d and O-2p electronic states. Optical analysis demonstrates strong ultraviolet absorption, low reflectance in the visible region, and a pronounced dielectric response, while elastic property evaluation confirms direction-dependent mechanical behavior due to elastic anisotropy. These results suggest that SrFe12O19 nanoparticles are promising candidates for advanced magnetic devices, optoelectronic applications, and microwave miniaturization technologies.
In developing nations, one of the most essential aspects of travel demand management (TDM) is the analysis of trip attraction in shopping malls. Shopping malls are major trip generators, as a large number of people visit these centers daily. A trip to the shopping center is usually seen as an essential travel due of its aim. As a result of its essential aspect, trip attraction plays a critical role inresolving and improving traffic problems through the development of road networks. Rapid urbanization and expansion have been noted in Chittagong city, one of Bangladesh's major cities, in recent decades due to enticing employment possibilities and the provision of social services. This additional traffic might exacerbate the current congested situation. This resulted in an extra journey to Chittagong City's shopping center. In this study, an effort has been made to determine the trip attraction rates of shopping malls in Chittagong city. The trip attraction rate is determined by physical features such as floor area (ft2), parking space, number of stores, and number of staff (per shop). This study also develops trip attraction models using Generalized Linear Modeling (GLM) with respect to physical features and Socio-demographic variables. The number of vehicles and people entering the shopping mall during peak hours on weekends and weekdays is counted every 15 minutes for first attempt. Questionnaire as technique used for data collection for second attempt. Highest trip attraction rate at weekend and weekday is Sanmer Ocean City with 286.8 PCU/hr. and 211.1 PCU/hr.In every case, the weekend trip rate is higher than the weekday travel rate. To estimate the trip attractions of shopping centers, four regression models were developed with respect to physical features and Socio-demographic variables. Floor area, number of shops, number of employees from physical characteristics have a strong correlation with number of trips. Job, income and shoppingexpense from socio-demographic features have a strong correlation with number of trips. This trip attraction rate can be used to estimate traffic flow and assess traffic impacts in the surroundings of a new shopping mall. The model assists in the understanding of tripping chains in the Shopping Center. The frequency of activity in the shopping center can be estimated using the model.
This study suggests a computationalized process of determining and modifying Mughal architecture motifs and design patterns with the help of Artificial Intelligence. Mughal ornamentation (floral arabesque, jali patterns, calligraphic panels, and Pietra dura inlays) is examined with CNN models trained on transfer learning (VGG16 and ResNet50). The trained models have classification accuracy of 98.6%, which indicates that the trained models are reliable to identify motifs even in small datasets. After classification, generative tools with AI assistance are used to generate variations of motifs, which are optimized with the help of scalable textile and surface design applications using the workflow of vectors based on the CAD format. Methodology provides a systematic line of information flow between architectural heritage recording and modern fashion making allowing to incorporate Mughal patterns into the sarees, dupattas, shawls, and heritage-based clothing. The study includes a repeatable AI- based methodology that satisfies cultural heritage, deep learning, and contemporary fashion design.
Acrophobia, or fear of heights, is a common and impairing specific phobia. Virtual reality exposure therapy (VRET) offers controlled immersive heightsimulations that may overcome practical and acceptability barriers of in vivo exposure. This review synthesizes randomized controlled trials, controlled comparative studies, pilot studies, case reports, and meta-analyses evaluating VRET for acrophobia. Across these studies, VRET reliably reduces height-related anxiety, behavioral avoidance, and catastrophic cognitions, with effects that are large relative to no- treatment or waitlist controls. In head-to-head comparisons, outcomes for VRET are generally comparable to traditional in vivo exposure, with no significant differences observed on standard acrophobia outcome measures (e.g., Acrophobia Questionnaire, Attitude Towards Heights Questionnaire, Behavioral Avoidance Test). Adverse effects are uncommon and typically mild. Short-term follow-ups suggest maintenance of gains after treatment. Taken together, the evidence indicates that VRET is an effective,acceptable, and scalable option for acrophobia, suitable for clinical settings where real-world height exposures are difficult to deliver. Future work should clarify the durability of benefits over longer intervals and identify patient and treatment factors (e.g., sense of presence, guidance format) that optimize outcomes.
The primary objective of concrete curing is to enhance the strength and durability of concrete components used in construction. This study investigated the efficiency of a solar-generated electric curing system compared with conventional water ponding forconventional and metakaolin-based concrete. Concrete beams (100 × 100 × 400 mm, 40 MPa) were cast, with half cured by ponding for 28 days and the rest subjected to solar-electric curing for three days. Flexural strength tests and statistical analyses (ANOVA) revealedsignificant differences among curing methods and mix types (F(3,8) = 32.56, p = 7.83 × 10−5). Water-cured specimens generally exhibited higher flexural strengths, but electrically cured metakaolin concrete achieved 88.8% of the 28-day water-cured strength within just three days, with mean values of 3.90 ± 0.18 MPa compared to 4.38 ± 0.07 MPa for water curing. The inclusion of 20% metakaolin enhanced hydration kinetics, microstructural densification, and water resistance, leading to improved mechanical anddurability performance. These findings demonstrate the efficiency of solar-electric curing in accelerating strength development while conserving time, water, and energy. Overall, the results confirm that solar-electric curing is a sustainable, energy-efficient, and technically viable alternative to conventional ponding, particularly in regions with limited water availability and abundant solar resources.
