Particularly in validating image integrity, advances in digital image analysis have profoundly affected forensic investigation. The growing reliance on digital image technology can be attributed in part to the broad availability of consistent and effective image-capturing technologies. The simplicity of changing image content thanks to advanced image-editing technologies presents fresh difficulties for forensic analysis. A structured hybrid framework is presented for finding important objects in images. It does this by using fast Fourier transformation (FFT) for frequency domain filtering, scale-invariant feature transformation (SIFT), and oriented FAST and rotated BRIEF (ORB) to pull out key points. The MobilenetV2 and VGG16 models extract features from key point areas to detect copy-move forgery. After that, an attention mechanism combines and normalizes these aspects. Key point matching uses the Euclidean distance; DBSCAN clustering groups pertinent key points for object localization. The suggested approach shows better performance than current methods and detects image copy-move forgery rather successfully. The framework's robustness is verified against image blurring, contrast alteration, color reduction, image compression, and brightness change among other post-processing techniques. Since photographs are altered, traditional approaches can struggle with a lot of variety; however, the proposed method combines advanced deep learning models and clustering techniques to make detection more accurate. Extensive testing on five benchmark copy-move forgeries datasets reveals that the suggested strategy may beat present techniques. This work offers a sophisticated automated approach to guarantee digital image integrity and identify image manipulation.

Tracking across multiple cameras is a complex problem in computer vision. Its main challenges include camera calibration, occlusion handling, camera overlap and field of view, person re-identification, and data association. In this study, we designed a laboratory as a research environment that facilitates our exploration of some of the above challenging issues. This study uses stereo camera calibration and key point detection to reconstruct the three-dimensional key points of the person being tracked, thereby performing person-tracking tasks. The results show that the dual cameras’ 3D spatial tracking method can have a relatively better continuous monitoring effect than a single camera alone. This study adopts four ways to evaluate person similarity, which can effectively reduce the unnecessary identity generation of persons. However, using all four methods simultaneously may not produce better results than a specific assessment method alone due to differences in people’s activity situations.

In line with the principles of the circular economy, this study aimed to develop a pyrolysis-activation regeneration process capable of producing highly porous carbon materials from spent granular activated carbon (GAC), which was generated by a high-tech electronics manufacturing company in Taiwan. Thermogravimetric analysis (TGA) and other thermochemical analyses were first conducted to investigate the thermal decomposition behavior of the spent GAC. Subsequently, the thermal regeneration system was employed to perform the N2 pyrolysis and CO2 activation experiments under various process conditions (i.e., 800, 850, and 900 °C for holding 0, 30, and 60 min, respectively). Analytical instruments included a surface area and porosimeter for pore property analysis, scanning electron microscopy (SEM) for porous texture observation, and energy dispersive X-ray spectroscopy (EDS) for surface elemental distribution analysis. The results revealed that the pore properties of thermally regenerated GAC were significantly improved compared to the spent GAC, indicating the effective removal or decomposition of adsorbed organics and deposited substances under the process conditions. Additionally, thermal regeneration via physical activation with CO2 led to enhanced pore properties compared to simple pyrolysis. The maximum BET surface area achieved exceeded 720 m2/g, which was greater than those of spent GAC (approximately 425 m2/g) and N2-pyrolyzed GAC (approximately 570 m2/g) under the same regeneration conditions (i.e., 900 °C with a 30 min holding time).

Cause-related marketing is one of the corporate social responsibility strategies that helps build corporate reputation, brand, and image. To understand the influencing factors before purchase actions in cause-related marketing, this study investigates whether consumers’ purchase intention is affected by brand and personal emotions. The “questionnaire survey method” was adopted for data collection, with a total of 379 valid samples retrieved for hypothesis testing. The partial least squares (PLS) analysis tool was used for verification. The results indicate that cause-related marketing activities have a positive impact on brand impression, and brands influence purchase intention. Additionally, emotions play a mediating role between cause-related marketing activities and purchase intention. This research contributes to understanding the relationship between cause-related marketing, brands, and purchase intention. This research also contributes to understanding the corporate comprehension of cause-related marketing strategies and provides reference for sustainable development research.

This study investigated a factory fire that resulted in an unusual situation that caused the deaths of two firefighters. The official fire investigation report was analyzed, records were obtained, and on-site investigations and interviews were conducted. Using these additional data and a calorimetric analysis to determine the combustibility of goods stored in the building at the time, a functional 3D model was produced, and a fire dynamics simulator (FDS) was run. The model was augmented using the results of calorimetric experiments for three types of primary goods being stored in the warehouse area: paper lunch boxes, tissue paper, and corrugated boxes. The reaction heat data obtained for each of the three sample types was 848.24, 468.29, and 301.21 J g−1, respectively. The maximum mass loss data were 98.522, 84.439, and 90.811 mass% for each of the three types, respectively. A full-scale fire scene reconstruction confirmed the fire propagation routes and changes in fire hazard factors, such as indoor temperature, visibility, and carbon monoxide concentration. The FDS results were compared to the NIST recommended values for firefighter heat exposure time. The cause of death for both firefighters was also investigated in terms of the heat resistance of the facepiece lenses of their self-contained breathing apparatus. Based on the findings of this study, recommendations can be made to forestall the recurrence of similar events.

In the current paper, an analysis of magnetohydrodynamic Williamson nanofluid boundary layer flow is presented, with multiple slips in a porous medium, using a newly designed human-brain-inspired Ricker wavelet neural network solver. The solver employs a hybrid approach that combines genetic algorithms, serving as a global search method, with sequential quadratic programming, which functions as a local optimization technique. The heat and mass transportation effects are examined through a stretchable surface with radiation, thermal, and velocity slip effects. The primary flow equations, originally expressed as partial differential equations (PDEs), are changed into a dimensionless nonlinear system of ordinary differential equations (ODEs) via similarity transformations. These ODEs are then numerically solved with the proposed computational approach. The current study has significant applications in a variety of practical engineering and industrial scenarios, including thermal energy systems, biomedical cooling devices, and enhanced oil recovery techniques, where the control and optimization of heat and mass transport in complex fluid environments are essential. The numerical outcomes gathered through the designed scheme are compared with reference results acquired through Adam’s numerical method in terms of graphs and tables of absolute errors. The rapid convergence, effectiveness, and stability of the suggested solver are analyzed using various statistical and performance operators.

As Deep Neural Network (DNN) models became more complex, the escalating computational demands on hardware made DNN accelerators a critical research topic. The rapid growth of DNN models required DNN accelerators to keep pace with these computational demands. However, the cost of hardware design was significant, and hardware and software were tightly coupled in the design of DNN accelerators. Much research on HW/SW co-design was evident, highlighting the importance of having a comprehensive framework to help find the optimal hardware and software design during the design phase. The cost models used in most of the current research relied on data reuse and mathematical estimation to calculate costs, an approach that was fast but inaccurate. In this article, we propose a framework for HW/SW co-design and introduce a hybrid cost model based on Gem5 that provides fast and precise performance evaluation. The framework uses a memory-centric approach to accurately model off-chip memory behavior. In addition, we discuss how to find the best design in a large co-design space and integrate a design point through a traffic generator and a cost model. Finally, we demonstrate that our framework can accurately assist DNN accelerator developers in exploring the optimal hardware and software co-design quickly and efficiently.