The quantitative crack test procedure commenced with the conversion of images containing identified cracks into grayscale representations, and subsequently, these were transformed into binary images using local thresholding. Employing Canny and morphological edge detection algorithms on the binary images, two distinct crack edge visualizations were then produced. The planar marker method and total station measurement method were subsequently applied to determine the actual size of the fractured edge image. A 92% accuracy rate was observed in the model, with width measurements demonstrating precision down to 0.22 mm, according to the results. Accordingly, the proposed approach makes possible bridge inspections and the gathering of objective and quantitative data.

KNL1 (kinetochore scaffold 1), a protein integral to the outer kinetochore, has been extensively researched, and a better understanding of its functional domains is emerging, predominantly in the context of cancer studies; however, its involvement in male fertility remains relatively underexplored. Through computer-aided sperm analysis (CASA), KNL1 was initially linked to male reproductive function. Mice lacking KNL1 function exhibited both oligospermia and asthenospermia, with a significant 865% decrease in total sperm count and a marked 824% increase in the number of static sperm. On top of that, an innovative method, combining flow cytometry and immunofluorescence, was designed to identify the aberrant stage within the spermatogenic cycle. The findings pointed to a 495% decline in haploid sperm and a 532% increment in diploid sperm numbers after the disruption of KNL1 function. Anomalies in the spindle's assembly and separation process were the cause of arrested spermatocytes during spermatogenesis, specifically at the meiotic prophase I stage. Ultimately, our findings revealed a connection between KNL1 and male fertility, offering guidance for future genetic counseling in cases of oligospermia and asthenospermia, and providing a robust approach for further investigating spermatogenic dysfunction through the application of flow cytometry and immunofluorescence.

Computer vision applications, including image retrieval, pose estimation, object detection in videos and still images, object detection within video frames, face recognition, and video action recognition, all address the challenge of activity recognition in UAV surveillance. Identifying and distinguishing human behaviors from video footage captured by aerial vehicles in UAV surveillance systems presents a significant difficulty. This research utilizes a hybrid model, a combination of Histogram of Oriented Gradients (HOG), Mask-RCNN, and Bi-Directional Long Short-Term Memory (Bi-LSTM), to recognize single and multi-human activities using aerial data input. Employing the HOG algorithm to extract patterns, the system uses Mask-RCNN to extract feature maps from the raw aerial data, and the Bi-LSTM network then analyzes the temporal relationships between the video frames, thereby determining the actions within the scene. Due to its bidirectional processing, this Bi-LSTM network minimizes error to a remarkable degree. Employing a histogram gradient-based instance segmentation, this novel architectural design elevates segmentation precision and enhances the accuracy of human activity classification using a Bi-LSTM approach. The experiments' results showcase that the proposed model performs better than alternative state-of-the-art models, obtaining a 99.25% accuracy score on the YouTube-Aerial dataset.

To counteract the detrimental effects of temperature stratification on plant growth in wintertime indoor smart farms, this study proposes an air circulation system, featuring a 6-meter width, 12-meter length, and 25-meter height, which forcibly transports the lowest, coldest air upwards. Through refinement of the manufactured air-circulation vent's geometry, this study also hoped to lessen the temperature difference between the top and bottom levels of the targeted interior space. see more A design of experiment based on an L9 orthogonal array table was implemented, which allowed the study of three levels for each design variable, including blade angle, blade number, output height, and flow radius. Flow analysis was applied to the nine models' experiments with the aim of reducing the substantial time and cost implications. The analytical data facilitated the creation of an optimized prototype using the Taguchi method. Further experimentation involved the deployment of 54 temperature sensors in an indoor setting to ascertain, over time, the difference in temperature between the upper and lower portions of the space, for the purpose of evaluating the prototype's performance. The temperature deviation under natural convection conditions reached a minimum of 22°C, with the thermal differential between the uppermost and lowermost areas maintaining a constant value. When an outlet shape was absent, as seen in vertical fans, the minimum temperature deviation observed was 0.8°C. Achieving a temperature difference of less than 2°C required at least 530 seconds. By implementing the proposed air circulation system, a reduction in both summer cooling and winter heating costs is anticipated. This reduction is directly attributed to the outlet shape, which minimizes the arrival time difference and temperature gradient between the top and bottom of the space, in comparison to systems lacking this design aspect.

Employing a BPSK sequence originating from the 192-bit AES-192 algorithm, this research examines radar signal modulation as a strategy for resolving Doppler and range ambiguities. A single, sharp main lobe, a consequence of the non-periodic AES-192 BPSK sequence's structure in the matched filter, is accompanied by periodic sidelobes, which a CLEAN algorithm can counteract. Evaluation of the AES-192 BPSK sequence's performance is conducted in juxtaposition to an Ipatov-Barker Hybrid BPSK code. This approach boasts an increased maximum unambiguous range, but at the cost of more demanding signal processing requirements. see more A BPSK sequence, secured by AES-192, lacks a maximum unambiguous range limitation, and randomizing pulse placement within the Pulse Repetition Interval (PRI) substantially broadens the upper limit on the maximum unambiguous Doppler frequency shift.

The facet-based two-scale model (FTSM) is a common technique in simulating SAR images of the anisotropic ocean surface. This model's operation is influenced by the cutoff parameter and facet size, with no prescribed method for selecting these critical values. To improve simulation efficiency, we suggest an approximation of the cutoff invariant two-scale model (CITSM), ensuring the model retains its robustness to cutoff wavenumbers. Additionally, the capability to withstand varying facet dimensions is achieved by adjusting the geometrical optics (GO) model, incorporating the slope probability density function (PDF) correction generated by the spectral distribution within each facet. Comparisons against sophisticated analytical models and experimental data reveal the new FTSM's viability, owing to its diminished dependence on cutoff parameters and facet sizes. Lastly, we present SAR images of the ocean surface and ship wakes, with diverse facet sizes, to validate the operational feasibility and applicability of our model.

Underwater object detection plays a significant role in the engineering of intelligent underwater vehicles. see more Object detection in underwater environments faces a combination of obstacles, including blurry underwater imagery, dense concentrations of small targets, and the constrained computational capabilities available on deployed hardware. We propose a new strategy for improving the performance of underwater object detection, which integrates a novel detection neural network, TC-YOLO, with adaptive histogram equalization for image enhancement and an optimal transport-based label assignment. The TC-YOLO network, a proposed architecture, was constructed using YOLOv5s as its foundation. In the new network's backbone and neck, transformer self-attention and coordinate attention, respectively, were incorporated to improve feature extraction for underwater objects. Label assignment through optimal transport techniques significantly reduces the number of fuzzy boxes, thus improving the efficiency of training data. The RUIE2020 dataset and ablation experiments strongly support our method's superior performance in underwater object detection compared to the original YOLOv5s and similar models. Importantly, this superior performance comes with a small model size and low computational cost, making it well-suited for mobile underwater applications.

Offshore gas exploration, fueled by recent years, has brought about a growing risk of subsea gas leaks, which could jeopardize human life, corporate holdings, and the environment. While optical imaging has become a common method for monitoring underwater gas leaks, substantial labor costs and a high occurrence of false alarms remain problematic due to the performance and assessment skills of the personnel involved in the operation. An advanced computer vision system for automatic, real-time underwater gas leak monitoring was the focus of this study's development. A performance comparison was made between Faster R-CNN and YOLOv4, two prominent deep learning object detection architectures. The optimal model for the real-time, automated detection of underwater gas leaks turned out to be the Faster R-CNN model, constructed with a 1280×720 image size and zero noise. From real-world data sets, this exemplary model could precisely classify and pinpoint locations of leaking underwater gas plumes, both small and large in scale.

The emergence of more and more complex applications requiring substantial computational power and rapid response time has manifested as a common deficiency in the processing power and energy available from user devices. A potent solution to this phenomenon is offered by mobile edge computing (MEC). Task execution efficiency is augmented by MEC, which moves certain tasks to edge servers for their execution. This paper studies the device-to-device (D2D) enabled mobile edge computing (MEC) network communications, with a focus on subtask offloading strategy and power allocation schemes for user devices.