Search

Carbon nano-onions (CNOs) are distinct from other carbon nanomaterials due to their distinctive physical and morphological features. Their addition can substantially increase the thermal, mechanical, tribological, and electrical properties. The present work explores the Milling machinability of zero-dimensional CNO reinforced epoxy nanocomposites. The effect of varying parameters, namely, the weight percentage of CNO (wt%), spindle speed (N), feed rate (F), and depth of cut (D), was examined to control the machining performances. Milling tests were conducted using the Taguchi-based L27Orthogonal array (OA), and a nonlinear regression model was used to develop a correlation between machining constraints and responses. A comparatively advanced metaheuristic multi-objective whale optimization algorithm (MOWOA) is used to accomplish an optimal parametric set. This optimization methodology was exploited to achieve non-dominated solutions and established the Pareto front. In the end, combined compromise solution (CoCoSo) was utilized to locate the most relevant result from the Pareto optimum setting. Based on the CoCoSo analysis, the optimal solution was found as CNO ≈ 1.5 wt%, spindle speed ≈ 1500 rpm, feed rate = 50 mm/min, and depth of cut ≈ 2 mm. The findings of the microscopy test support the results of the proposed optimization tool. The suggested MOW optimization procedure could be used in the production sector for monitoring quality and productivity indices. The proposed hybrid module can be forwarded to the industrial sector to optimize the multiple conflicting responses.

Poultry behavior indicates their health, welfare, and production performance. Timely access to broilers’ behavioral information can improve their welfare and reduce disease spread. Most behaviors require a period of observation before they can be accurately judged. However, the existing approaches for multi-object behavior recognition were mostly developed based on a single-frame image and ignored the temporal features in videos, which led to misrecognition. This study proposed an end-to-end method for recognizing multiple simultaneous behavioral events of cage-free broilers in videos by Broiler Behavior Recognition System (BBRS) based on spatiotemporal feature learning. The BBRS consisted of three main components: the improved YOLOv8s detector, the Bytetrack tracker, and the 3D-ResNet50-TSAM model. The basic network YOLOv8s was improved with MPDIoU to identify multiple broilers in the same frame of videos. The Bytetrack tracker was used to track each identified broiler and acquire its image sequence of 32 continuous frames as input for the 3D-ResNet50-TSAM model. To accurately recognize behavior of each tracked broiler, the 3D-ResNet50-TSAM model integrated a temporal-spatial attention module for learning the spatiotemporal features from its image sequence and enhancing inference ability in the case of its image sequence less than 32 continuous frames due to its tracker ID switching. Each component of BBRS was trained and tested with the rearing density of 7-8 birds/m2. The results demonstrated that the mAP@0.5 of the improved YOLOv8s detector was 99.50%. The Bytetrack tracker achieved a mean MOTA of 93.89% at different levels of occlusion. The Accuracy, Precision, Recall, and F1scoreof the 3D-ResNet50-TSAM model were 97.84%, 97.72%, 97.65%, and 97.68%, respectively. The BBRS showed satisfactory inference ability with an Accuracyof 93.98% when 26 continuous frames of the tracked broiler were received by the 3D-ResNet50-TSAM model. This study provides an efficient tool for automatically and accurately recognizing behaviors of cage-free multi-broilers in videos. The code will be released on GitHub (https://github.com/CoderYLH/BBRS) as soon as the study is published.

The zero-point positioning system (Z-PCS) is more suitable for the increasingly common production modes of multiple types, small batches, and changing conditions than to conventional fixtures. Currently, the Z-PCS cannot provide sufficient clamping force, and the design of the positioning structure is prone to over-positioning. The working principles of each functional module in the system are unclear, making it difficult to promote product design optimization and troubleshooting. This article aims to design a high-performance Z-PCS, revealing the structural composition and working principle of the system, including the clamping structure, positioning structure, and air circuitry arrangement. During the design process, the finite element numerical calculations were adopted to verify the mechanical properties of each key load-bearing component. Finally, the designed product underwent positioning accuracy and clamping force testing. The results indicate that the Z-PCS designed in this article can provide a clamping force of at least 73.7 kN and control the repetitive positioning error below 0.002 mm. Micro-deformation grooves can be adaptively added to compensate for the over-positioning error. Heat-treated martensitic-type stainless steel is an ideal material for constructing the body of a high-performance Z-PCS.

Nickel-Titanium smart alloys gradually widen their industrial demand due to their inherent structural and functional characteristics. Due to advanced properties such as shape memory effect (SME), superelasticity (SE), corrosion resistance, and better damping property, NiTi is the focus of interest among all the advanced materials. The domain of additive manufacturing (AM) is explored as the 3D print designed parts that sweep the conventional subtractive manufacturing process. Currently, smart structure fabrication by AM is a novel approach. This review paper gives a foundational understanding of NiTi smart alloy fabrication by various AM processes and their application in health care industry. 4D printing methodologies, especially the selective laser melting (SLM) process, are carefully discussed here by considering the sensing parameters for NiTi.

Dehalogenative aryl–aryl coupling has been the key strategy in on-surface synthesis for crafting covalently bonded carbon-based nanomaterials. However, comprehensive studies focusing on the kinetics of these reactions are notably rare. Moreover, the existing literature predominantly addresses debromination, often neglecting dechlorination, a critical reaction requiring higher energies but with profound implications for environmental protection and advancement of sustainable chemistry practices. Here, we combined synchrotron-based X-ray photoelectron spectroscopy (XPS) and scanning tunneling microscopy (STM) to study the dehalogenated coupling reactions of chlorinated and brominated aromatic hydrocarbons on the Au(111) surface. The high-resolution surface-sensitive techniques allow to identify the reactants and products. We find that extended annealing facilitates setting the activation temperature for dechlorinated coupling to around 473 K, a notable decrease from the previously considered 573 K, highlighting the significant impact of kinetic effects on the on-surface reaction process. Using the temperature-programmed XPS measurement, we are able to extract the kinetic curves of the dechlorinated coupling reactions and obtain detailed insight into the reaction process. This research enhances our mechanistic understanding of the widely utilized on-surface dehalogenative coupling reaction, serving as a fundamental step toward optimizing its efficiency and selectivity.