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Energy Consumption (EC) in the process of mechanical manufacturing directly leads to environmental pollution and resource waste. However, the EC characteristics of machine tool processing are complex, and most energy-saving optimization models require accurate material performance data and cutting force models. In response to the above issues, the study first analyzes the structural composition of the machining system, clarifies the main variable parameters for optimization, and then establishes a mathematical model with the determined optimization variables to describe the EC characteristics. Finally, the established optimization model is solved using the adaptive particle swarm algorithm to find the optimal combination of process parameters and achieve energy-saving optimization. The improved adaptive particle swarm intelligence algorithm tends to converge after more than 50 iterations. When taking low cost and low EC as the optimization goal, the cutting EC of the optimization solution is 3.49 × 10⁵ J, the processing time is 42.68 s, and the processing cost is 46.71 points, and the processing cost and EC are between the single optimization goal of low cost and low EC. It is indicated that the proposed method provides a reasonable energy-saving optimization strategy for machining process parameters, and provides support for the implementation of energy-saving optimization of machining center process parameters.

The rise of IoT devices has led to a surge in data generation, necessitating efficient processing solutions. Traditional cloud-centric approaches face challenges like latency, bandwidth and privacy issues. Edge computing, a promising paradigm, enables data processing closer to the source, enhancing IoT-driven computer vision applications. This shift integrates edge computing frameworks with a study that proposed a novel drosophila food search-tuned convolutional neural network (DFS-CNN) and computer vision algorithms for real-time tasks like object detection and anomaly detection. We collected acquisition data from labeled Faces in the Wild (LFW) video and image dataset, the acquisition data were preprocessed using a bilateral filter for minimizing noise while maintaining sharp edges, and then filtered data were extracted using a histogram of oriented gradients (HOG) — the DFS-CNN model using the HOG feature set to detection the computer vision solution. The optimal DFS-CNN model was deployed on edge computing and is used in an IoT-based architecture to compute and transport data for real-time performance simulation using Tensor Flow Lite. The proposed method is compared to other traditional algorithms. The proposed DFS-CNN model detects objects in the LWT image with 96% accuracy. The proposed DFS-CNN model was used to address students’ inactivity status during the online exam, and that the outcome of the suggested method was tested with data latency, and real-time response, according to a comparative performance analysis.

Traditional Chinese medicine (TCM) has played important roles in health protection and disease treatment for thousands of years in China and has gained the gradual acceptance of the international community. However, many intricate issues, which cannot be explained by traditional methods, still remain, thus, new ideas and technologies are needed. As an emerging system biology technology, the holistic view adopted by metabolomics is similar to that of TCM, which allows us to investigate TCM with complicated conditions and multiple factors in depth. In this paper, we tried to give a timely and comprehensive update about the methodology progression of metabolomics, as well as its applications, in different fields of TCM studies including quality control, processing, safety and efficacy evaluation. The herbs investigated by metabolomics were selected for detailed examination, including Anemarrhena asphodeloides Bunge, Atractylodes macrocephala Kidd, Pinellia ternate, etc.; furthermore, some valuable results have been obtained and summarized. In conclusion, although the study of metabolomics is at the early phase and requires further scrutiny and validation, it still provides bright prospects to dissect the synergistic action of multiple components from TCM. Overall, with the further development of analytical techniques, especially multi-analysis techniques, we expect that metabolomics will greatly promote TCM research and the establishment of international standards, which is beneficial to TCM modernization.

Fe and Fe-based alloys are great candidates for biodegradable metal implants. In order to achieve high density pure Fe and Fe-35Mn structures, the correct implementation of process strategies and parameters needs to be studied to achieve high density parts. In this study, pure Fe and Fe-35Mn were successfully processed via SLM with a relative density over 99.5%. The lower melting temperature of Fe-35Mn introduced porosity, resulting in a lower processability compared to the pure Fe. In terms of process optimization, most of the typical quality indicators (roughness, density and apparent density) could be correlated to the energy density.

This paper shows the problem of possible existence of statistical self-similarity and long-range (long-term) dependencies in behavior of computer memory systems. It will be shown, that the hierarchical structure of memory and the dispersion of time constants during accessing successive levels of such a structure, lead to "memory effect" and self-similarity. These effects have not been taken into account so far in computer systems design, management and description. A few methods were used to calculate the Hurst H coefficient, which reflects the statistical self-similarity in time series and the d parameter that represents the problem of long-range dependencies.

Although image retrieval for e-commerce field has a huge commercial potential, e-commerce oriented content-based image retrieval is still very raw. Modern online shopping systems have certain limitations. In particular, they use conventional tag-based retrieval and lack making use of visual content. The paper presents a methodology to retrieve images of shopping items based on fuzzy dominant colors. People regard color as an aesthetic issue, especially when it comes to choosing the colors of their clothing, apartment design and other objects around. No doubt, color inuences purchasing behavior — to a certain extent, it is a reection of human's likes and dislikes. The fuzzy color model that we are proposing represents the collection of fuzzy sets, providing the conceptual quantization of crisp HSI space having soft boundaries. The proposed method has two parts: assigning a fuzzy colorimetric profile to the image and processing the user query. We also use underlying mechanisms of attention from a theory of visual attention, like perceptual categorization. Subjectivity and sensitivity of humans in color perception and bridging the semantic gap between low-level color visual features and high-level concepts are major issues that we plan to tackle in this research.

In this work, aluminum/titanium carbide (Al/TiC) surface composite has been fabricated by friction stir processing using a novel modular Direct Particle Injection Tool (DPI–FSP). The tool has a unique feature wherein the TiC particles have been transferred from the tool itself by spring adjusted plunger movement into the matrix. The microstructural observations from optical and scanning electron microscope (SEM)-EDS results revealed the homogeneous distribution of particles in the stirred zone (SZ) and the thickness of the formed surface composite layer (SCL) is approximately 0.34mm. X-ray diffraction results confirmed that the particles are reinforced in the aluminum matrix, and no intermetallics have been formed in the composite. The microhardness of composite was increased from 68 to 135Hv, and the impact test results showed that the toughness was almost comparable to that of the base metal.

We demonstrate a new instance of useful-noise effect or stochastic resonance, occurring in magnetic resonance imaging (MRI). Based on the physics of signal–noise coupling specific to MRI, we establish the possibility of regimes where nonlinear post-processing can benefit from an increase in the level of the noise present in the MRI apparatus. The validation is obtained by both theoretical analysis and experimental observations. We especially show that the beneficial tuning of the noise can be practically achieved by controlling the bandwidth of the sampling receiver of the MRI apparatus. These results constitute a nontrivial extension of stochastic resonance in the domain of images, arising here with a signal–noise coupling in MRI which is distinct from the purely additive or multiplicative couplings previously investigated in the framework of useful-noise effect.

Integrated circuit manufacturing is characterized by decreasing feature dimensions, ever-increasing sophistication of processing equipment, more and more demanding cleanliness requirements, and exponentially increasing costs of fabrication lines. These requirements force us to re-examine the fundamental assumptions underlying the current integrated circuit manufacturing practice. We review one promising alternative which satisfies many if not all of the requirements of the integrated circuit manufacture of the coming decade. The continuous processing of silicon wafers in a controlled environment, often dubbed ‘factory-in-a-bottle’, promises to move us from the mass manufacturing mindset to a lean manufacturing mindset which has the potential of flexibility, reliability, and profitability without relying on the size of the silicon fabrication line.

Magnesium-based metal matrix composites (MMCs) have been receiving attention in recent years as potential materials for aerospace and automobile applications because of their low density and superior specific properties. In the present study, magnesium based metal matrix composites containing up to 21.3 weight percentage of SiC particulates were successfully synthesized using disintegrated melt deposition technique. Microstructural characterization studies conducted on the composite samples revealed a uniform distribution of SiC particulates, finite amount of porosity and good particulate/matrix interfacial integrity. Results of thermal analysis and mechanical properties showed an increase in elastic modulus, no significant change in the yield strength, and decrease in coefficient of thermal expansion, ultimate tensile strength and ductility with an increase in the weight percent of the SiC particulates. An attempt is made to correlate the physical and mechanical properties obtained with the microstructural characteristics of the composites.

Porous bioactive ceramic materials can be very useful on the filling of bone defects, as scaffolds for tissue engineering, or as carrier systems for the delivery of drugs. The present work describe an innovative methodology for producing porous bioactive ceramic structures, starting from hydroxylapatite or bioactive glass-ceramic powders, that present an adequate micro and macroporosity combined with compressive mechanical properties matching those of cancellous bone. The described processing route is based on a microwave baking process using a powder containing corn starch, sodium pyrophosphate and sodium bicarbonate as blowing agent, and can be used to produce either hydroxylapatite based or glass-ceramic porous structures. By using this new methodology it was possible to produce both porous bioactive glass-ceramic and HA/TCP bi-phasic structures with adequate micro and macroporosity combined with mechanical properties that will eventually allow for their successful use in a range of biomedical applications.

The unmanned aerial vehicle integral impeller is the key part of the turbine machinery. In order to obtain the desired dynamics, integral impeller blades with a large twist angle, root fillet and other structures are used, which increases the difficulty of milling. Therefore, to create an integral impeller consistent with design requirements needs not only a good manufacturing method, but also a good process to guarantee this. In this paper, a detailed processing analysis of the unmanned aerial vehicle integral impeller is made on the 5-axis machining center. The process route of NC machining is formulated by the selection of machine tools, blank, technology datum, cutting tools, tool path, cutting parameters and machining allowance. Therefore, the tool paths of rough machining, semi-finishing, finishing, and clean-up machining are generated by Power Mill software. Finally, the machining scheme is verified by experiment, and the rationality of the processing is proved.