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As an integral part of production planning control, order release management is critical to enhance the competitiveness and production efficiency of companies. Previous literature shows limited application of optimization-based models in assembly job shops, primarily due to the intricate nature of product structures and assembly operations. Therefore, based on the idea of the allocated clearing function (ACF) model, we introduce material flow constraints and complex assembly structure constraints during the assembly stage, proposing the assembly job shop allocated clearing function (AACF) model. The performance of the AACF model and the rule-based mechanisms in terms of cost and timing measures are compared through experiments containing 6 factors and 96 scenarios. The results show that the AACF model performs better in terms of cost management, service level and order due date deviation. In addition, a sensitivity analysis of the objective function parameters is performed to confirm the robustness of the AACF model. Finally, a case application in a real assembly shop illustrates the feasibility and validity of the proposed AACF model.

The purpose of this study was to investigate the influence of smartphone reading on the ocular surface and to compare the various effects of different screens and light conditions on the ocular surface. One hundred nineteen volunteers were randomly divided into: light + organic light‐emitting diode (OLED), light + electronic ink (eINK), dark + OLED, and dark + eINK. Ocular surface examinations, including noninvasive break‐up time (NIBUT), noninvasive keratograph tear meniscus height (NIKTMH), ocular redness, fluorescein break‐up time (FBUT), corneal fluorescein staining, meibomian gland assessment, Schirmer I Test, and blinking frequency, were performed before and after a reading task. Symptoms were evaluated using the Ocular Surface Disease Index (OSDI) and Computer Vision Syndrome Questionnaire (CVS‐Q). NIBUT and FBUT were decreased statistically significantly after participants read on an OLED screen for 2 hours compared with the baseline in light and dark environments, whereas no statistically significant decrease was observed on an eINK screen. NIKTMH was statistically significantly decreased after reading on an OLED screen in light and dark settings, and the eINK screen had a lesser effect on NIKTMH. An obvious increase in the ocular redness, OSDI and CVS‐Q scores was observed after reading on an OLED screen, whereas the eINK screen had a lesser effect on these indicators. Blink rate increased gradually in OLED subgroups during the reading task, whereas no statistically significant difference was observed in the eINK subgroups. Our research suggested that reading on an OLED screen can cause ocular surface disorder and obvious subjective discomfort, whereas reading on an eINK screen can minimize ocular surface disorder in both dark and light environments.

Basal cell carcinoma (BCC) is the most common malignancy worldwide, with increasing incidence. BCCs present low mortality but high morbidity, and its pathogenesis remains unclear. Eph receptors have been implicated in tumorigenesis. EphA7 plays a role as a tumor suppressor in certain cancers. We checked EphA7 expression levels and methylation status in a set of BCCs, benign skin diseases, and compound nevus tissue samples using immunohistochemistry. EphA7 protein was positively expressed in normal basal cells, benign skin diseases, and compound nevus cells, but lost in areas of BCC tissues. We detected hypermethylation in BCC tissue samples with reduced expression of EphA7. There is a significant relationship between the expression level of EphA7 receptor protein and the methylation status of CpG islands in the EphA7 promoter region (P

Brassica napus L. is a vital plant oil resource worldwide. The fatty acid biosynthesis and oil accumulation in its seeds are controlled by several genetic and environmental factors, including daytime and nighttime temperatures. We analyzed changes in oleic and erucic acid content in two double haploid (DH) lines, DH0729, a weakly temperature-sensitive line, and DH0815, a strongly temperature-sensitive line, derived from B. napus plants grown at different altitudes (1600, 1800, 2000, 2200, and 2400 m a.s.l., 28.85° N, 112.35° E) and nighttime temperatures (20/18, 20/16, 20/13 and 20/10 °C, daytime/nighttime temperature). Based on medium- and long-chain fatty acid metabolites, the total oleic acid content 35 and 43 days after flowering was significantly lower in low nighttime temperature (LNT, 20/13 °C) plants than in high nighttime temperature (HNT, 20/18 °C) plants (HNT: 58–62%; LNT: 49–54%; an average decrease of 9%), and the total erucic acid content was significantly lower in HNT than in LNT plants (HNT: 1–2%; LNT: 8–13%; an average increase of 10%). An RNA-seq analysis showed that the expression levels of SAD (LOC106366808), ECR (LOC106396280), KCS (LOC106419344), KAR (LOC106367337), HB1(LOC106430193), and DOF5 (LOC111211868) in STSL seeds increased under LNT conditions. In STSL seeds, a base mutation in the cis-acting element involved in low-temperature responsiveness (LTR), the HB1 and KCS promoter caused loss of sensitivity to low temperatures, whereas that of the KCS promoter caused increased sensitivity to low temperatures.

Background/Aims: Diabetic foot ulcers (DFUs) present a major challenge in clinical practice, and hyperglycemia-induced angiogenesis disturbance and endothelial dysfunction likely exacerbate DFUs. The long-acting glucagon-like peptide-1 (GLP-1) analog liraglutide (Lira) is a potential activator of AMP-activated protein kinase (AMPK) that appears to enhance endothelial function and have substantial pro-angiogenesis and antioxidant stress effects. Therefore, in this study, we aimed to investigate whether the protective role of Lira in diabetic wound healing acts against the mechanisms underlying hyperglycemia-induced endothelial dysfunction and angiogenesis disturbance.Methods: Accordingly, db/db mice were assessed after receiving subcutaneous Lira injections. We also cultured human umbilical vein endothelial cells (HUVECs) in either normal or high glucose (5.5 or 33 mM glucose, respectively) medium with or without Lira for 72 h.Results: An obvious inhibition of hyperglycemia-triggered endothelial dysfunction and angiogenesis disturbance was observed; follow by a promotion of diabetic wound healing under Lira treatment combined with restored hyperglycemia-impaired AMPK signaling pathway activity. AMPKα1/2 siRNA and Compound C (Cpd C), an inhibitor of AMPK, abolished both Lira-mediated endothelial protection and pro-angiogenesis action, as well as the diabetic wound healing promoted by Lira. Furthermore, hypoxia inducible factor-1α (Hif-1α; transcription factors of AMPK substrates) knockdown in HUVECs and db/db mice demonstrated that Lira activated AMPK to prevent hyperglycemia-triggered endothelial dysfunction and angiogenesis disturbance, with a subsequent promotion of diabetic wound healing that was Hif-1α–heme oxygenase-1 (HO-1) axis-dependent. Taken together, these findings reveal that the promotion of diabetic wound healing by Lira occurs via its AMPK-dependent endothelial protection and pro-angiogenic effects, which are regulated by the Hif-1α–HO-1 axis.

Health degradation monitoring of machining tools is of great importance in industrial application field. In this paper, a novel multisensory data-driven health degradation monitoring system schema for the machining tools is proposed by using a generalized multiclass support vector machine (GenSVM). In this schema, multidimensional feature extraction is implemented in the time domain, frequency domain, and time-frequency domain based on the time domain statistical analysis, power spectrum analysis, and complete ensemble empirical mode decomposition with adaptive noise, respectively. On the basis, effective features that are strongly associated with the degradation process are picked out using a Pearson correlation coefficient. Meanwhile, a new and flexible GenSVM model is constructed to identify the health state of machining tools, which integrates a simplex encoding and iterative majorization optimization algorithm. A practical application case study is implemented to verify the effectiveness of the proposed method. The results show the superior performance of the proposed method compared with other standard methods.

Health degradation monitoring of rolling element bearing (REB) is of great significance to ensure safety and availability of mechanical equipment. This paper presents a new online health degradation monitoring method of REB based on growing self-organizing mapping (GSOM) and clustered support vector machine (CSVM). In the proposed method, multidimensional health degradation features of the REB are extracted to reflect health degradation process, including time-domain statistical features, frequency spectrum features, intrinsic mode function energy features, wavelet packet frequency band energy features. Multiple GSOMs are utilized to adaptively fuse each kind of the extracted features for the health indices. CSVM is constructed to achieve accurate health status identification of the REB. A health degradation experimental case of the REB is analyzed to demonstrate the effectiveness of the proposed method. The results show that the proposed method has obvious superior performance compared to other existing methods.

Traumatic brain injury (TBI) is a clinical emergency with a very high incidence, disability, and fatality rate. Minocycline, a widely used semisynthetic second-generation tetracycline antibiotic, has anti-inflammatory and bactericidal effects. However, minocycline has not been explored as a therapeutic drug in TBI and if effective, the related molecular mechanism is also unclear. In this study, we examined the neuroprotective effect and possible mechanism of minocycline, in mice TBI model by studying the trauma-related functional and morphological changes. Also, in vitro cell studies were carried out to verify the animal model data. We found that minocycline significantly improved the neurobehavioral score, inhibited apoptosis, repaired the blood-brain barrier, and reduced the levels of inflammatory factors Interleukin-6 and tumor necrosis factor-α in TBI mice. In vitro, upon oxygen and glucose deprivation, minocycline reduced the levels of cellular inflammatory factors and increased the levels of tight junction and adherens junction proteins, thereby significantly improving the cell viability. Moreover, Mino treatment prevented the loss of tight junction and adherens junction proteins which were markedly reversed by an ER stress activator (tunicamycin) both in vivo and in vitro. Our findings set an effective basis for the clinical use of Mino to treat Traumatic brain injury-induced neurological deficits.

To solve the lack of consideration of the learning time sequence and knowledge dependencies in group-based recommendation, we proposed a novel group-oriented recommendation algorithm which is characterized by mapping the user's learning log to a personal learning generative network (PLGN) based on a knowledge map. In this paper, we first provide calculation methods of similarity and temporal correlation between knowledge points, where we provide the construction method of the PLGN. Second, a method for measuring the similarities between any two PLGNs is proposed. According to the similarities, we perform the CURE clustering algorithm to obtain learning groups. Third, based on the group clustering, the group learning generative network using a graph overlay method is generated. We calculate the importance of the vertices on the different learning needs and propose a group-oriented recommendation algorithm. Finally, we compare the effect of the proposed recommendation to that of a group-based collaborative filtering recommendation for the aspects of precision rate, recall rate, normalized discounted cumulative gain, and the average accuracy of parameters (MAP). The experimental results show that the group-oriented learning recommendation based on the learning generated network outperforms the group recommendation-based collaborative filtering when the amount of data is large enough.

Condition-based maintenance is an effective method for deciding when to maintain deteriorating system. In this paper, a non-periodic maintenance model with adaptive inspection intervals is proposed by the consideration of system stability and deterioration. Different maintenance plans and monitoring strategies are adopted in distinct stages of life cycle of the deteriorating system to reduce costs, which is a complex multiple optimization parameter problem. And particle swarm optimization algorithm combining heuristic rules is designed to solve this multi-objective problem. Finally, a numerical example is implemented to illustrate the effectiveness and rationality of the proposed model. The comparison between the proposed model and other maintenance models illustrates the economy of the proposed model and the importance of considering system stability. Sensitivity analysis is also performed to investigate the effect of seven cost factors.

Learning resource recommendation, such as curriculum video recommendation, is an effective way to reduce cognitive overload in online learning. The existing curriculum video recommendation systems are generally limited to one course, ignoring the knowledge correlation between courses. In this paper, we propose a two-stage cross-curriculum video recommendation algorithm that considers both the learners' implicit feedback and the knowledge association between course videos. First, we use collaborative filtering to generate a video seed set, which is based on the learner's implicit video feedback, such as video learning frequencies, video learning duration and video pausing and dragging frequencies. Second, we construct a cross-curriculum video-associated knowledge map and use a random walk algorithm to measure the relevance of the course videos. The relevance is based on each video seed as a starting node and is extended to a video subgraph. Then, several cross-curricular video-oriented subgraphs are recommended for the learners. The experimental results indicate that our cross-curriculum video recommendation algorithm performs better than the traditional collaborative filtering-based recommendation algorithms in terms of accuracy, recall rate, and knowledge relevance.

Interactions between nanoparticles is one of the key factors governing their assembly for ordered structures. Understanding such interactions between non-spherical nanoparticles and developing a quantitative force model are critical to achieving the ordered structures for various applications. In the present study, the non-contact interactions of two identical gold nanorods (AuNRs) with different aspect ratios have been studied by molecular dynamics simulation. A new interaction potential and force model for two nanorods approaching side-by-side has been proposed as a function of particle surface separation and their relative orientation. In addition, the interaction potentials of two nanorods approaching in other typical orientation configurations (i.e., crossed, head-to-head and head-to-side) have also been investigated.

Graphene-encapsulated iron nanoparticles (Fe(G)) hold great promise as microwave absorbers owing to the combined dielectric loss of the graphene shell and the magnetic loss of the ferromagnetic metal core. Transmission electron microscopy (TEM) revealed transition metal nanoparticles encapsulated by graphene layers. The microwave electromagnetic parameters and reflection loss (R) of the Fe(G) were investigated. Graphene provided Fe(G) with a distinctive dielectric behavior via interfacial polarizations taking place at the interface between the iron cores and the graphene shells. The R of Fe(G)/paraffin composites with different Fe(G) contents and coating thickness was simulated according to the transmit-line theory and the measured complex permittivity and permeability. The Fe(G)/paraffin composites showed an excellent microwave absorption with a minimum calculated R of −58 dB at 11 GHz and a 60 wt% Fe(G) loading. The composites showed a wide bandwidth (the bandwidth of less than −10 dB was about 11 GHz). The R of composites with 1–3 mm coating thickness was measured using the Arch method. The absorption position was in line with the calculated results, suggesting that the graphene-coated iron nanoparticles can generate a suitable electromagnetic match and provide an intense microwave absorption. Excellent Fe(G) microwave absorbers can be obtained by selecting optimum layer numbers and Fe(G) loadings in the composites.

This paper focuses on the positioning algorithm suitable for harsh indoor environment such as manufacturing workshop in which the interferences from different directions cannot be neglected. The positioning algorithm is an improved Received Signal Strength Indication- (RSSI-) based ranging method. To preprocess the RSSI data Gaussian filter and mean filter are adopted. A sector transmission model is constructed and applied to divide the area around the anchor node into several sectors, and the shadowing model of each sector is measured. Then the average of all RSSI values in a particular sector is considered as the final RSSI value for distance calculation. The calculated distances designated for trilateration are also optimized to eliminate the abnormal distances from positioning calculations. Positioning experiment is designed via ZigBee facilities. The results show that the proposed algorithm greatly improves the positioning accuracy and stability; for instance, the average positioning error is reduced from 1.32 m to 0.79 m. Moreover, this study also proves that the node position has great influence on the positioning accuracy.

Traditional workflow models have obvious shortcomings in describing complex workflows. Such complexity is due not only to the hierarchical property of business process, but also to the complicated dependencies among tasks. In this paper, we first introduce a conceptual task model based on UML class graph. Then we apply colored Petri nets to model task interfaces and composite task versions which form Task State Nets (TSNs) and Task Version Nets (TVNs). The main advantage is that they explicitly express all task interfaces´ internal states and thus they can easily describe the complex control dependencies. Next we further combine TSN/TVN with place refining mechanism and bring forward our Hierarchical Task State Net (HTSN). It supports the description of workflow's hierarchical structure, multi-versions configuration of composite task as well as the analysis of structural properties. Finally, we give a case study.