SEGi University

The inability to physically interact with products remains a fundamental barrier to virtual commerce, particularly for weight-a key cue for quality and value. This study validates a low-cost, multisensory pseudo-haptic solution using standard VR controllers to simulate product weight. Through an experiment (N = 120) manipulating the control-display (C/D) ratio across 16 product categories, we demonstrate that pseudo-haptic "heft" significantly increases purchase intent and perceived value. This effect is serially mediated by perceived scarcity and is substantially stronger for hedonic (e.g., luxury goods) versus utilitarian products. The findings provide an evidence-based, hardware-agnostic design protocol (optimal C/D range: 0.6-1.4) for enhancing sensory marketing in virtual retail.

In recent years, the role of fostering problem-solving abilities in science classrooms has taken on increasing importance, especially in the rural setting, with students often having fewer resources. The traditional teacher approach may support students with tasks in retaining information, but often does not develop student involvement in assessing, evaluating, and investigating. There is no doubt that the effect of Inquiry-Based Science Instruction (IBSI) on problem-solving kids' development in a rural primary school in China, where children of farmers are studying, has been one of the topics of research. The research goes on to take a look at the possibility of different measurements for the engagement, reasoning, and application of scientific ideas under the two teaching methods, one with and one without inquiry. A mixed-methods framework was applied to the study, in which qualitative and quantitative data were considered. The study shaped a pre- and post-test activity to investigate the improvement of problem-solving in the four domains: Improvement of Problem-Solving, Growth Compared, Reasoning & Inquiry Engagement, and diversity of strategies. Statistical approximations of meaning were guided by paired t-tests and effect sizes. For the qualitative data collection, semi-structured student interviews and classroom observations were thematically analysed to maximise the depth of student experience and teacher opinion. Results indicated that statistically meaningful improvements occurred in all four areas explored: Problem-Solving Improvement (1.5238, 1.8270); Comparative Growth (1.3982, 1.7006); Reasoning and Inquiry Engagement (1.4770, 1.7796); and Strategy Diversity (1.4732, 1.7762), which were also supported with large effect sizes. According to the qualitative results, an upsurge in student motivation and self-esteem, along with better teamwork, were the main positive impacts. All in all, the results suggest that IBSI is the one generating an active learning floor that lets children's minds and cooperation through problem-solving to flourish in the rural classrooms.

Despite increased preventive measures and awareness, romance scams continue to target victims worldwide, emphasizing the need for a deeper understanding of their underlying mechanisms. This study applied Conversation Analysis and Van Dijk's Discursive Interaction and Manipulation framework to examine 30 authentic conversations obtained from the Royale Malaysia Police and actual victims. The finding identified three types of romance scams and revealed that scammers managed conversations in a step-by-step manner, building trust and emotional connection, shaping interactions, and influencing victims' perceptions. The study also identifies sequential stages of scams and highlights how scammers exploit power asymmetries, narratives, and relational engagement to advance their objectives. These insights provide a nuanced understanding of online romance scams and can inform targeted prevention and awareness efforts. By linking empirical conversation patterns to socio-cognitive mechanisms of manipulative, this research demonstrates how scammers gain both financial and psychological leverage over victims.

Oil leakage in oil-immersed power transformers poses a significant threat to grid reliability, potentially causing severe electrical accidents and environmental pollution if not detected in time. Detecting oil leakage outdoors, however, remains challenging due to the impact of weather conditions such as fog, humidity, and rain, which obscure the leakage signs and complicate real-time detection. To address these challenges, we propose a solution that integrates infrared thermal imaging with a CNN-SVM hybrid architecture. The core of this approach lies in shifting from traditional Softmax-cross-entropy-based empirical risk minimization (ERM) to maximum-margin-based structural risk minimization (SRM). A fully fine-tuned MobileNetV3 transforms low-contrast, boundary-softened infrared thermal images-often affected by fog and moisture-into a more discriminative high-dimensional feature space, where positive and negative samples become linearly separable. This is followed by replacing Softmax with a linear SVM and using hinge loss to enforce a margin constraint, which maximizes the classification margin and improves robustness to input perturbations. Experimental results show that our proposed method outperforms all compared models, achieving an accuracy of 0.990, significantly higher than ResNet50_BCE (0.908), EfficientNetB0 (0.925), YOLOv11n-CLS (0.930), and ViT (0.929). In terms of F1-Score (0.989) and AUC (0.995), MobileNetV3-SVM also demonstrates excellent performance, ensuring outstanding classification capability. Additionally, the model achieves an inference latency of only 6.3 ms, demonstrating excellent real-time inference performance, highlighting its potential for transformer oil monitoring applications. This research contributes to SDG 6 by preventing industrial water pollution resulting from transformer oil runoff, thereby protecting vital water sources in remote environments.

The one-stage object detection method based on deep learning has been widely used in remote sensing image object detection because of its high efficiency. Although it has achieved good results in the field of remote sensing image object detection, it is aimed at the problems of poor recognition and insufficient accuracy of small objects in remote sensing images or complex scenes. Firstly, the FreqFusion module and BiFPN module based on frequency feature fusion were designed to replace the original neck structure, which effectively enhanced the characterization ability of multi-scale features. Secondly, the CAA cross-dimensional attention mechanism was introduced to reconstruct the detection head to realize the adaptive feature calibration of the channel and spatial dimensions. Finally, the mean average precision (mAP) of FBC-YOLO11 on the DIOR dataset is 83.5%, which is 4% higher than that of the YOLO11 algorithm, and the recall rate is increased by 2.6% compared with the YOLO11 algorithm. The effectiveness of the method in remote sensing small object detection is proved.