Multi-objective optimization of energy consumption and surface quality in nanofluid SQCL assisted face milling

Considering the significance of improving the energy efficiency, surface quality and material removal quantity of machining processes, the present study is conducted in the form of an experimental investigation and a multi-objective optimization. The experiments were conducted by face milling AISI 1045 steel on a Computer Numerical Controlled (CNC) milling machine using a carbide cutting tool. The Cu-nano-fluid, dispersed in distilled water, was impinged in small quantity cooling lubrication (SQCL) spray applied to the cutting zone. The data of surface roughness and active cutting energy were measured while the material removal rate was calculated. A multi-objective optimization was performed by the integration of the Taguchi method, Grey Relational Analysis (GRA), and the Non-Dominated Sorting Genetic Algorithm (NSGA-II). The optimum results calculated were a cutting speed of 1200 rev/min, a feed rate of 320 mm/min, a depth of cut of 0.5 mm, and a width of cut of 15 mm. It was also endowed with a 20.7% reduction in energy consumption. Furthermore, the use of SQCL promoted sustainable manufacturing. The novelty of the work is in reducing energy consumption under nano fluid assisted machining while paying adequate attention to material removal quantity and the product’s surface quality

Gut Microbiota Is a Major Contributor to Adiposity in Pigs

Different breeds of pigs vary greatly in their propensity for adiposity. Gut microbiota is known to play an important role in modulating host physiology including fat metabolism. However, the relative contribution of gut microbiota to lipogenic characteristics of pigs remains elusive. In this study, we transplanted fecal microbiota of adult Jinhua and Landrace pigs, two breeds of pigs with distinct lipogenic phenotypes, to antibiotic-treated mice. Our results indicated that, 4 weeks after fecal transplantation, the mice receiving Jinhua pigs’ “obese” microbiota (JM) exhibited a different intestinal bacterial community structure from those receiving Landrace pigs’ “lean” microbiota (LM). Notably, an elevated ratio of Firmicutes to Bacteroidetes and a significant diminishment of Akkermansia were observed in JM mice relative to LM mice. Importantly, mouse recipients resembled their respective porcine donors in many of the lipogenic characteristics. Similar to Jinhua pig donors, JM mice had elevated lipid and triglyceride levels and the lipoprotein lipase activity in the liver. Enhanced expression of multiple key lipogenic genes and reduced angiopoietin-like 4 (Angptl4) mRNA expression were also observed in JM mice, relative to those in LM mice. These results collectively suggested that gut microbiota of Jinhua pigs is more capable of enhancing lipogenesis than that of Landrace pigs. Transferability of the lipogenic phenotype across species further indicated that gut microbiota plays a major role in contributing to adiposity in pigs. Manipulation of intestinal microbiota will, therefore, have a profound impact on altering host metabolism and adipogenesis, with an important implication in the treatment of human overweight and obesity

Service-Oriented Node Scheduling Scheme for Wireless Sensor Networks Using Markov Random Field Model

Future wireless sensor networks are expected to provide various sensing services and energy efficiency is one of the most important criterions. The node scheduling strategy aims to increase network lifetime by selecting a set of sensor nodes to provide the required sensing services in a periodic manner. In this paper, we are concerned with the service-oriented node scheduling problem to provide multiple sensing services while maximizing the network lifetime. We firstly introduce how to model the data correlation for different services by using Markov Random Field (MRF) model. Secondly, we formulate the service-oriented node scheduling issue into three different problems, namely, the multi-service data denoising problem which aims at minimizing the noise level of sensed data, the representative node selection problem concerning with selecting a number of active nodes while determining the services they provide, and the multi-service node scheduling problem which aims at maximizing the network lifetime. Thirdly, we propose a Multi-service Data Denoising (MDD) algorithm, a novel multi-service Representative node Selection and service Determination (RSD) algorithm, and a novel MRF-based Multi-service Node Scheduling (MMNS) scheme to solve the above three problems respectively. Finally, extensive experiments demonstrate that the proposed scheme efficiently extends the network lifetime.This work is supported by the National Science Foundation of China under Grand No. 61370210 and the Development Foundation of Educational Committee of Fujian Province under Grand No. 2012JA12027.Cheng, H.; Su, Z.; Lloret, J.; Chen, G. (2014). Service-Oriented Node Scheduling Scheme for Wireless Sensor Networks Using Markov Random Field Model. Sensors. 14(11):20940-20962. https://doi.org/10.3390/s141120940S2094020962141

A 3D Organically Synthesised Porous Carbon Material for Lithium Ion Batteries

We report the first organically synthesized sp–sp3 hybridized porous carbon, OSPC‐1. This new carbon shows electron conductivity, high porosity, the highest uptake of lithium ions of any carbon material to‐date, and the ability to inhibit dangerous lithium dendrite formation. The new carbon exhibits exceptional potential as anode material for lithium‐ion batteries (LIBs) with high capacity, excellent rate capability, long cycle life, and potential for improved safety performance

Genomic data for 78 chickens from 14 populations

Background: Since the domestication of the red jungle fowls (Gallus gallus; dating back to~10 000 B.P.) in Asia, domestic chickens (Gallus gallus domesticus) have been subjected to the combined effects of natural selection and human-driven artificial selection; this has resulted in marked phenotypic diversity in a number of traits, including behavior, body composition, egg production, and skin color. Population genomic variations through diversifying selection have not been fully investigated. Findings: The whole genomes of 78 domestic chickens were sequenced to an average of 18-fold coverage for each bird. By combining this data with publicly available genomes of five wild red jungle fowls and eight Xishuangbanna game fowls, we conducted a comprehensive comparative genomics analysis of 91 chickens from 17 populations. After aligning ~21.30 gigabases (Gb) of high-quality data from each individual to the reference chicken genome, we identified ~6.44 million (M) single nucleotide polymorphisms (SNPs) for each population. These SNPs included 1.10 M novel SNPs in 17 populations that were absent in the current chicken dbSNP (Build 145) entries. Conclusions: The current data is important for population genetics and further studies in chickens and will serve as a valuable resource for investigating diversifying selection and candidate genes for selective breeding in chickens.Peer reviewedAnimal Scienc

Discussion on early warning method of coal and gas outburst

Definition of early warning of coal and gas outburst was given, and difference between the early warning and forecasting was clearly pointed out. The early warning of coal and gas outburst was classified from angle of space, time and index system, and features of the early warning were put forward according to specific characteristics of coal and gas outburst disaster. System structure and implementation of the early warning were analyzed guided by system theory and accident theory. Steps of the early warning were described from five aspects of detection of risk source, warning sign identification, analysis of warning situation, alert release and early warning response. Method of effect evaluation of the early warning was put forward by using three indicators of initial warning accuracy, false negative rate and false alarm rate. The field application results show that the average accuracy rate of state warning is 89.1%, the average accuracy rate of trend warning is 92.5%, and the false negative rate is 0

Experimental Study on Fabrication of CVD Diamond Micro Milling Tool by Picosecond Pulsed Laser

Because of the many advantages of high-precision micromachining, picosecond pulsed lasers (PSPLs) can be used to process chemical-vapor-deposited diamonds (CVD-D). With the appropriate PSPL manufacturing technique, sharp and smooth edges of CVD-D micro tools can be generated. In this study, a PSPL is used to cut CVD-D. To optimize PSPL cutting, the effects of its parameters including fluence, pulse pitch, and wavelength on the cutting results were investigated. The results showed that the wavelength had the greatest impact on the sharpness of CVD-D. With PSPL cutting, sharp cutting edges, and smooth fabricated surfaces of the CVD-D, micro tools were achieved. Finally, the fabrication of CVD-D micro milling tools and micro milling experiments were also demonstrated