A framework for smart production-logistics systems based on CPS and industrial IoT

Industrial Internet of Things (IIoT) has received increasing attention from both academia and industry. However, several challenges including excessively long waiting time and a serious waste of energy still exist in the IIoT-based integration between production and logistics in job shops. To address these challenges, a framework depicting the mechanism and methodology of smart production-logistics systems is proposed to implement intelligent modeling of key manufacturing resources and investigate self-organizing configuration mechanisms. A data-driven model based on analytical target cascading is developed to implement the self-organizing configuration. A case study based on a Chinese engine manufacturer is presented to validate the feasibility and evaluate the performance of the proposed framework and the developed method. The results show that the manufacturing time and the energy consumption are reduced and the computing time is reasonable. This paper potentially enables manufacturers to deploy IIoT-based applications and improve the efficiency of production-logistics systems

Short-Term Effects of Phosphorus Application on Phosphorus Content in Soil and Dominant Species under Ungrazed and Grazed Conditions in the Tibetan Plateau

Phosphorus (P) availability in soils is an important indicator for health and growth in plants. The available phosphorus (AP) content of alpine meadow soil in northern China is low. Previous studies of alpine meadow have reported that soil nutrient levels have been significantly improved after a 9-year enclosure (Wu et al. 2010b) whereas continuous grazing over 19 years reduced total P (TP) by 25% in a Leymus chinensis steppe (Li 2001). Many studies have shown that the application of P fertilizer can improve dry matter production and forage quality in cultivated grasslands (Shi et al. 2007). However, the benefits of applying P fertiliser to alpine meadow in terms of increases in soil AP content and P concentrations in plants in both grazed and ungrazed meadows are less well known. The objective of this study was to investigate the short-term effects of P application on the AP content in top soil and total P content in the foliage of dominant meadow plant species

Effects of Alternate Furrow Irrigation on the Forage Quality of Alfalfa (\u3cem\u3eMedicago sativa\u3c/em\u3e)

Alfalfa (Medicago sativa) is widely used to establish pasture to sustain animal production because of its high nutritional quality for livestock (Bouton, 2012). Therefore improving quality of alfalfa pasture is the main goal for establishing pasture throughout the world. Irrigation is an important approach for achieving high qualities of alfalfa pasture, however, the scarcity of water threatens the sustainability of world alfalfa production, requiring us to force on using water more efficiently (Liu and Guo, 2013). Alternate furrow irrigation has been proved as a water-saving technique to promote fruit quality of tomatoes and grapevines and the seed quality of cotton (Kirda et al., 2004, Dos Santos et al., 2003, Tang et al., 2005). But these studies only focus on harvesting the seed, fruits and tubers, while neglecting the vegetative mass. In this study, we investigated the effects of alternate furrow irrigation with different irrigation volumes on the quality of alfalfa

Reinforcement learning based optimal decision making towards product lifecycle sustainability

Artificial intelligence (AI) has been widely used in robotics, automation, finance, healthcare, etc. However, using AI for decision-making in sustainable product lifecycle operations is still challenging. One major challenge relates to the scarcity and uncertainties of data across the product lifecycle. This paper aims to develop a method that can adopt the most suitable AI techniques to support decision-making for sustainable operations based on the available lifecycle data. It identifies the key lifecycle stages in which AI, especially reinforcement learning (RL), can support decision-making. Then, a generalised procedure of using RL for decision support is proposed based on available lifecycle data, such as operation and maintenance data. The method has been validated in a case study of an international vehicle manufacturer, combined with modelling and simulation. The case study demonstrates the effectiveness of the method and identifies that RL is the current most appropriate method for maintenance scheduling based on limited available lifecycle data. This paper contributes to knowledge by demonstrating an empirically grounded industrial case using RL to optimise decision-making for improved product lifecycle sustainability by effectively prolonging the product lifetime and reducing environmental impact.Vinnova [2017-01649]

Transparent Perfect Microwave Absorber Employing Asymmetric Resonance Cavity

The demand for high‐performance absorbers in the microwave frequencies, which can reduce undesirable radiation that interferes with electronic system operation, has attracted increasing interest in recent years. However, most devices implemented so far are opaque, limiting their use in optical applications that require high visible transparency. Here, a scheme is demonstrated for microwave absorbers featuring high transparency in the visible range, near‐unity absorption (≈99.5% absorption at 13.75 GHz with 3.6 GHz effective bandwidth) in the Ku‐band, and hence excellent electromagnetic interference shielding performance (≈26 dB). The device is based on an asymmetric Fabry–Pérot cavity, which incorporates a monolayer graphene and a transparent ultrathin (8 nm) doped silver layer as absorber and reflector, and fused silica as the middle dielectric layer. Guided by derived formulism, this asymmetric cavity is demonstrated with microwaves near‐perfectly and exclusively absorbs in the ultrathin graphene film. The peak absorption frequency of the cavity can be readily tuned by simply changing the thickness of the dielectric spacer. The approach provides a viable solution for a new type of microwave absorber with high visible transmittance, paving the way towards applications in the area of optics.A general strategy is presented to design a new type of microwave absorber based on an asymmetric Fabry–Pérot resonant cavity by employing monolayer graphene, transparent spacer, and ultrathin doped Ag film. This asymmetric cavity is demonstrated with microwaves near‐perfectly and exclusively absorbs in the ultrathin graphene layer at resonances and maintains high visible transmittance.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/151816/1/advs1299-sup-0001-S1.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/151816/2/advs1299.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/151816/3/advs1299_am.pd

Cortical Neural Stem Cell Lineage Progression Is Regulated by Extrinsic Signaling Molecule Sonic Hedgehog.

Neural stem cells (NSCs) in the prenatal neocortex progressively generate different subtypes of glutamatergic projection neurons. Following that, NSCs have a major switch in their progenitor properties and produce γ-aminobutyric acid (GABAergic) interneurons for the olfactory bulb (OB), cortical oligodendrocytes, and astrocytes. Herein, we provide evidence for the molecular mechanism that underlies this switch in the state of neocortical NSCs. We show that, at around E16.5, mouse neocortical NSCs start to generate GSX2-expressing (GSX2+) intermediate progenitor cells (IPCs). In vivo lineage-tracing study revealed that GSX2+ IPC population gives rise not only to OB interneurons but also to cortical oligodendrocytes and astrocytes, suggesting that they are a tri-potential population. We demonstrated that Sonic hedgehog signaling is both necessary and sufficient for the generation of GSX2+ IPCs by reducing GLI3R protein levels. Using single-cell RNA sequencing, we identify the transcriptional profile of GSX2+ IPCs and the process of the lineage switch of cortical NSCs

The ovine HIAT1 gene: mRNA expression, InDel mutations, and growth trait associations

BackgroundThe hippocampal abundant transcript 1 (HIAT1) gene, also known as major facilitator superfamily domain-containing 14A (MFSD14A), encodes for a transmembrane transporter protein and has been previously shown to be associated with milk production in buffalo and sheep breeds, as well as growth traits in chicken and goats. However, tissue level distribution of the ovine HIAT1 gene, as well as its effect on body morphometric traits in sheep, has yet to be studied.MethodsThe HIAT1 mRNA expression profile of Lanzhou fat-tailed (LFT) sheep was determined by quantitative real-time PCR (qPCR). A total of 1498 sheep of three indigenous Chinese sheep breeds were PCR-genotyped for polymorphisms of HIAT1 gene. Student's t-test was used to observe the association between the genotype and sheep morphometric traits.ResultsHIAT1 was widely expressed in all examined tissues, and was particularly abundant in the testis of male LFT sheep. Additionally, a 9-bp insertion mutation (rs1089950828) located within the 5'-upstream region of HIAT1 was investigated in Luxi black-headed (LXBH) sheep and Guiqian semi-fine wool (GSFW) sheep. The wildtype allele frequency 'D' was found to be more prevalent than that of the mutant allele ‘I'. Furthermore, low genetic diversity was confirmed in all sampled sheep populations. Subsequent association analyses indicated an association between the 9-bp InDel mutation of interest and the morphometric traits of LXBH and GSFW sheep. Furthermore, yearling ewes with a heterozygous genotype (ID) demonstrated smaller body sizes, while yearling rams and adult ewes with the heterozygous genotype were found to have overall better growth performance.ConclusionThese findings imply that functional InDel polymorphism (rs1089950828) has the potential to be utilized for marker-assisted selection (MAS) of growth traits in domestic Chinese sheep populations

A multicentre single arm phase 2 trial of neoadjuvant pyrotinib and letrozole plus dalpiciclib for triple-positive breast cancer.

peer reviewedCurrent therapies for HER2-positive breast cancer have limited efficacy in patients with triple-positive breast cancer (TPBC). We conduct a multi-center single-arm phase 2 trial to test the efficacy and safety of an oral neoadjuvant therapy with pyrotinib, letrozole and dalpiciclib (a CDK4/6 inhibitor) in patients with treatment-naïve, stage II-III TPBC with a Karnofsky score of ≥70 (NCT04486911). The primary endpoint is the proportion of patients with pathological complete response (pCR) in the breast and axilla. The secondary endpoints include residual cancer burden (RCB)-0 or RCB-I, objective response rate (ORR), breast pCR (bpCR), safety and changes in molecular targets (Ki67) from baseline to surgery. Following 5 cycles of 4-week treatment, the results meet the primary endpoint with a pCR rate of 30.4% (24 of 79; 95% confidence interval (CI), 21.3-41.3). RCB-0/I is 55.7% (95% CI, 44.7-66.1). ORR is 87.4%, (95% CI, 78.1-93.2) and bpCR is 35.4% (95% CI, 25.8-46.5). The mean Ki67 expression reduces from 40.4% at baseline to 17.9% (P < 0.001) at time of surgery. The most frequent grade 3 or 4 adverse events are neutropenia, leukopenia, and diarrhoea. There is no serious adverse event- or treatment-related death. This fully oral, chemotherapy-free, triplet combined therapy has the potential to be an alternative neoadjuvant regimen for patients with TPBC

A Timed Colored Petri Net Simulation-Based Self-Adaptive Collaboration Method for Production-Logistics Systems

Complex and customized manufacturing requires a high level of collaboration between production and logistics in a flexible production system. With the widespread use of Internet of Things technology in manufacturing, a great amount of real-time and multi-source manufacturing data and logistics data is created, that can be used to perform production-logistics collaboration. To solve the aforementioned problems, this paper proposes a timed colored Petri net simulation-based self-adaptive collaboration method for Internet of Things-enabled production-logistics systems. The method combines the schedule of token sequences in the timed colored Petri net with real-time status of key production and logistics equipment. The key equipment is made ‘smart’ to actively publish or request logistics tasks. An integrated framework based on a cloud service platform is introduced to provide the basis for self-adaptive collaboration of production-logistics systems. A simulation experiment is conducted by using colored Petri nets (CPN) Tools to validate the performance and applicability of the proposed method. Computational experiments demonstrate that the proposed method outperforms the event-driven method in terms of reductions of waiting time, makespan, and electricity consumption. This proposed method is also applicable to other manufacturing systems to implement production-logistics collaboration