Engineering reports : open access最新文献

To address the challenge of detecting surface defects on printed circuit board (PCB), this paper proposes an improved method based on YOLOv5s. To enhance the detection of small target defects, the Coordinate Attention mechanism is integrated into the three Convolutional layers module of YOLOv5s, and the Normalized Gaussian Weighted Distance loss is introduced to replace the Complete Intersection over Union loss. To achieve a lightweight model with parameters reduced and to enhance detection speed for real-time applications and terminal deployment, the convolutional layers in the Neck module of YOLOv5s are replaced with Grouped Shuffled Convolution layers. Evaluated on two benchmark data sets, the PCB_DATASET and DeepPCB data sets, the improved model achieves 97.0% and 99.1% in [email protected] and achieves 163 and 167 in Frames Per Second, respectively. In addition, the model parameters are reduced to 6.6 million, meeting the demands of small target detection in real-time applications.

This research suggests a double-layer optimization operation approach that considers electric vehicle participation when low-carbon scheduling is used within the power system; there is a need to provide assistance in the transition to low-carbon energy sources. The Monte Carlo technique is used to simulate data for electric vehicle load predictions. The top model uses the grid operators as its central organization. It sets the lowest generation and carbon trading costs as its objective, engages directly in the carbon trading market, determines the ideal model for unit output distribution, and determines each unit's actual production. In the lower model, the operators of the electric vehicle cluster sense changes in the upper carbon emission factor signal, modify their charging behavior through demand response, calculate the single-day reduction of carbon emissions, and the attainment of the benefits associated with the mitigation of carbon exhausts. A carbon emissions model is used to assign the responsibility for carbon exhausts from the user side of the generator unit to the carbon discharge aspect mechanism. Four distinct scenarios are built up, illustrating the enhanced IEEE 14 node system, to examine and confirm the efficacy of the suggested optimum scheduling model.

This work presents an analysis of decode and forward (DF) relay-assisted device-to-device (D2D) communication over a novel fluctuating two-ray (FTR) faded channel affected by co-channel interference (CCI). CCI fading condition is assumed to follow a novel independent fluctuating two-ray (IFTR) model. The FTR model consists of dominant components that jointly fluctuate, plus a diffuse component. The IFTR model complements the FTR model by allowing the dominant components to fluctuate independently. Both models are typically incorporated in various environments because of their generalized nature. The contributions of this paper include analyses of relay-assisted D2D FTR/IFTR system with two cases. These cases are considered based on diversity schemes at the relay and D2D receiver: (A) Selection combining (SC) at relay and D2D receiver and (B) Maximal ratio combining (MRC) at relay and D2D receiver. Also, the expressions for outage probability, success probability and capacity with outage over Terahertz (THz) communication channels are derived by the help of characteristic function (CF). These expressions are functions of THz channel conditions, distances between various communication nodes of the system, diversity scheme parameters and various FTR/IFTR fading channel parameters. It is observed that the variation in CCI IFTR parameters slightly effect the overall performance of the D2D system. Furthermore, by increasing pointing errors of D2D signals system performance degrades. However, performance is improved when CCI pointing errors are increased.

This study focuses on optimizing the tensile performance of basalt/glass fiber-reinforced polymer composites enhanced with hybrid nanofillers, comprising equal proportions of multi-walled carbon nanotubes (MWCNTs) and silicon dioxide (SiO₂). The nanofiller content is evaluated at weight percentages of 0%, 1%, and 2%. Using response surface methodology (RSM), the research investigates the interactive effects of three key parameters: filler weight (0%–2%), molding pressure (5–15 MPa), and sonication time (10–30 min) on the mechanical performance of the composites. A Box–Benkhen design was adopted to develop predictive models and establish optimal processing conditions for maximizing the mechanical properties. The tensile test (as per ASTM D 638 standard) and scanning electron microscopy (SEM) were performed. It was found that filler weight plays a dominating role in the tensile performance of hybrid nanocomposites, followed by molding pressure and sonication time. A predictive mathematical model was developed for each response. The maximum tensile strength of 267 MPa and an elongation at failure of 2.25% were achieved at a filler weight of 1%, molding pressure of 15 MPa, and sonication time of 30 min, corresponding to run order 16. The hybrid nanofillers synergistically enhance the load transfer efficiency and interfacial bonding, as observed through microstructural analysis using SEM. Statistical analysis validated the accuracy and reliability of the developed models, demonstrating robust correlation coefficients between actual and predicted values. The results highlight the potential of RSM as a strong tool for optimizing hybrid nanocomposite properties, paving the way for advanced material design in structural applications.

Cancer is a deadly disease characterized by the uncontrolled growth and spread of abnormal cells. Tumors, the masses formed by these abnormal cells, can vary significantly in size, composition, and behavior. Understanding tumor dynamics is crucial for the development of effective treatments. A novel computational model is presented to analyze the evolution of tumor tissue over time, incorporating drug resistance and the convective mass flux of tumor cell movement for a more realistic representation of tumor dynamics. The governing equations are numerically solved using the finite difference method with forward-time central-space discretization. The predictive capabilities of the model were evaluated by investigating the impact of drug therapy on cell death and the sensitivity of the model's outcome to initial nutrient and drug concentrations. Key findings revealed that higher initial nutrient concentrations promote tumor growth, highlighting the importance of monitoring and managing nutrient levels in patients. The tumor consumes nutrients at a faster rate than they can diffuse inward, leading to nutrient gradients and potential necrosis in the core. High drug concentrations do not always correlate with increased cell death due to factors such as drug toxicity or resistance development. The relationship between drug concentration and cell death is nonlinear, suggesting that there might be an optimal drug concentration range to maximize efficacy. These insights offer valuable guidance for optimizing drug delivery and designing effective tumor control strategies. This study contributes to a deeper understanding of tumor growth and the development of more effective cancer treatments to improve patient outcomes. In addition, the proposed model serves as a valuable tool for researchers and clinicians to explore different treatment regimens and predict patient responses to therapy.

Developing a reliable kinetic model for these redox reactions is crucial for understanding and improving oxygen carriers practical in chemical looping applications. The traditional pore model assumes that the solid product forms a continuous layer uniformly covering the solid reactant surface during the gas–solid reactions, in the result the model fails to capture the kinetic transitions caused by the actual solid structure change. We integrated product island growth theory into random pore model (RPM). The model assumes the oxygen carrier has randomly distributed and overlapped pores, involving surface chemical reactions, product island growth, product layer diffusion, internal gas diffusion, and external gas diffusion to the particle surface. The model was verified using data of a natural iron ore from micro-fluidized bed thermogravimetric analysis (MFB-TGA) experiments. The kinetic parameters include chemical reaction rate constant ($��k_s�$), critical product layer thickness ($��h_C�$), and product layer diffusion coefficient ($��D_P�$). The reduction reaction rate is primarily governed by the chemical reaction, while both surface chemical reactions and product layer diffusion significantly influence the oxidation reaction. The reduction reaction, with an activation energy of 84.2 kJ/mol, is more temperature-sensitive than the oxidation reaction, which has an activation energy of 41.88 kJ/mol. Reaction temperature, particle size, and reactant gas concentration significantly impact the reaction rate and conversion of iron ore oxygen carriers. The model effectively predicts and analyzes the redox behavior of natural iron ore oxygen carrier, providing insights to optimize its performance.

This research introduces two conjugate gradient methods, BIV1 and BIV2, designed to enhance the efficiency and performance of unconstrained optimization problems with only first derivative vectors. The study explores the derivation of new conjugate gradient parameters and investigates their practical performance. The proposed BIV1 and BIV2 methods are compared with the traditional Hestenes-Stiefel (HS) method through a series of numerical experiments. These experiments evaluate the methods on various test problems sourced from the CUTE library and other unconstrained problem collections. Key performance metrics, including the number of iterations, function evaluations, and CPU time, demonstrate that both BIV1 and BIV2 methods offer superior efficiency and effectiveness compared to the HS method. Furthermore, the effectiveness of these methods is illustrated in the context of training artificial neural networks. Experimental results show that the new methods achieve competitive performance in terms of convergence rate and accuracy.

The use of large-scale agricultural machinery for plant protection in nonstandard orchards of mountainous areas is very limited, while small-wheeled robots have great application prospect. To address the false judgment of visual information caused by the shading of branches and leaves and the difficulty in avoiding obstacles in complex orchard terrain, an operation trajectory optimization approach based on the improved dynamic window algorithm (DWA) with ant colony optimization (ACO) was developed. First, the orchard environment information was acquired by laser radar, and the voxel method was used to reduce the point cloud density of orchard ground. The grid method was used to segment the point clouds. Using the K-means clustering algorithm to extract navigation lines of the tree row. Second, combining with the kinematic model and the motion path constraints of robots, a series of candidate trace points were generated using the model-based prediction algorithm (SBMPO). Then, using the improved ACO-DWA algorithm, the robot access cost was integrated into the objective function of the search node, and the path planning was carried out online based on the environment grid map. Finally, in simulation and orchard validation scenes, the effects of this improved approach were checked through the simulation platform Matlab R2021 and ROS2 operating system. Simulation results on Matlab R2021 show that this improved algorithm has an average of 28%, 16% and 37% reduction in three indicators of the travel path, the number of detours, and the calculation time, respectively. In the orchard real-time experiment, compared with other excellent algorithms, the navigation distance error is reduced obviously. These experimental results show that this method would obviously improve the robot access effect in the inner area between the tree row, significantly meet the requirements of high safety and speed level of robots in these scenes. Also, it could be applied in the field such as picking and spraying robots.

The study aimed to establish the adoption status of tools and practices, drivers, and barriers for Lean Manufacturing (LM) for the Tanzanian context based on the industry size. The study adopted a survey research design. A purposeful sampling technique was deployed to collect responses from micro, small, medium, and large manufacturing industries. A total of 256 responses were received. Upon data cleaning, a total of 243 responses remained for further analysis. SPSS version 27 was used to analyze descriptive statistics such as mean and inferential statistics such as ANOVA. The results indicate the growing adoption of Lean Manufacturing across industries, from micro to large industries. The adoption of Lean Manufacturing for large industries is 75.1%, for medium industries is 66.7%, for small industries is 54%, and 50% for micro industries. The average adoption for all industries is 61.5%. The specific Lean practices vary by industry size; common practices include 5 s, visual management, and concurrent engineering with larger industries using more complex lean practices like cellular manufacturing. Moreover, the study indicates that drivers and barriers for Lean Manufacturing vary based on the size of the manufacturing industry. The study will guide those who wish to implement Lean Manufacturing in Tanzania and other countries with similar characteristics and challenges.

In this paper, the ammonium polyphosphate (APP) is mixed with epoxy to fabricate APP/epoxy goat horn composites. The composites were obtained by casting method with different weight ratios of APP of 0%, 10%, 15%, and 20% for improving the mechanical and thermal properties. This is the first paper to discuss about both mechanical and thermal properties of APP/epoxy goat horn composites. The mechanical properties were evaluated by tensile test and wear test. For the goat horn composites with a content of 20 wt% of APP, the tensile strength was 30 MPa, shows that adding APP led to composites having a better tensile strength. From limiting oxygen index test results, it is clear that goat horn composite is fire retardant, and that the fire-retardant value improves with the addition of APP up to a weight ratio of 15% APP. The thermogravimetric analysis (TGA) reveals that the degradation of composites in relation to temperature. From the TGA analysis, the bio composites without APP gives the better results comparing with incorporation of APP in composites up to 40% of weight loss. At the same time, the 50% of weight loss occur at the degradation temperature of around 380°C for 20 wt% of APP composites. It shows the addition of APP improves the thermal stability of the composites. The ignition time from the horizontal flammability test of the composites of 0, 10, 15, and 20 wt% APP were 20, 22, 24, and 25 (in s) respectively. It shows that the addition of filler material into the composites, the ignition time also increases. The water absorption percentage was peak at 15% of APP and lower at 10% of APP. All the samples were saturated after 80 h. The lower percentage of water absorption in composites can be attributed to the improved interfacial bonding between the fibers and matrix. From scanning electron microscopy (SEM) analysis the morphological structure and dispersion of ammonium polyphosphate and goat horn particles in epoxy bio composites were studied.