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Large-scale monitoring of water quality parameters (WQPs) is one of the most critical issues for protecting and managing water resources. However, monitoring optically inactive WQPs, such as total nitrogen (TN), ammoniacal nitrogen (AN), and total phosphorus (TP) in inland waters, is still challenging. This study constructed retrieval models to explore the spatiotemporal evolution of TN, AN, and TP by Landsat 8 images, water quality sampling, and five machine learning algorithms (support vector regression, SVR; random forest regression, RFR; artificial neural networks, ANN; regression tree, RT; and gradient boosting machine, GBM) in the Nandu River downstream (NRD), a tropical river in China. The results indicated that these models can effectively monitor TN, AN, and TP concentrations at in situ sites. In particular, TN by RFR as well as AN and TP by ANN had better accuracy, in which the R2 value ranged between 0.44 and 0.67, and the RMSE was 0.03–0.33 mg/L in the testing dataset. The spatial distribution of TN, AN, and TP was seasonal in NRD from 2013–2022. TN and AN should be paid more attention to in normal wet seasons of urban and agricultural zones, respectively. TP, however, should be focus on in the normal season of agricultural zones. Temporally, AN decreased significantly in the normal and wet seasons while the others showed little change. These results could provide a large-scale spatial overview of the water quality, find the sensitive areas and periods of water pollution, and assist in identifying and controlling the non-point source pollution in the NRD. This study demonstrated that multispectral remote sensing and machine learning algorithms have great potential for monitoring optically inactive WQPs in tropical large-scale inland rivers.

Temperature stresses affect plant phenotypic diversity. The developmental stability of the inflorescence, required for reproductive success, is tightly regulated by the interplay of genetic and environmental factors. However, the mechanisms underpinning how plant inflorescence architecture responds to temperature are largely unknown. We demonstrate that the barley SEPALLATA MADS-box protein HvMADS1 is responsible for maintaining an unbranched spike architecture at high temperatures, while the loss-of-function mutant forms a branched inflorescence-like structure. HvMADS1 exhibits increased binding to target promoters via A-tract CArG-box motifs, which change conformation with temperature. Target genes for high-temperature-dependent HvMADS1 activation are predominantly associated with inflorescence differentiation and phytohormone signalling. HvMADS1 directly regulates the cytokinin-degrading enzyme HvCKX3 to integrate temperature response and cytokinin homeostasis, which is required to repress meristem cell cycle/division. Our findings reveal a mechanism by which genetic factors direct plant thermomorphogenesis, extending the recognized role of plant MADS-box proteins in floral development.

The shape of an inflorescence varies among cereals, ranging from a highly branched panicle in rice to a much more compact spike in barley (Hordeum vulgare L.) and wheat (Triticum aestivum L.). However, little is known about the molecular basis of cereal inflorescence architecture. We profiled transcriptomes at three developmental stages of the barley main shoot apex — spikelet initiation, floral organ differentiation, and floral organ growth — and compared them with those from vegetative seedling tissue. Transcript analyses identified 3688 genes differentially transcribed between the three meristem stages, with a further 1394 genes preferentially expressed in reproductive compared with vegetative tissue. Co-expression assembly and Gene Ontology analysis classified these 4888 genes into 28 clusters, revealing distinct patterns for genes such as transcription factors, histone modification, and cell-cycle progression specific for each stage of inflorescence development. We also compared expression patterns of VRS (SIX-ROWED SPIKE) genes and auxin-, gibberellic acid- and cytokinin-associated genes between two-rowed and six-rowed barley to describe regulators of lateral spikelet fertility. Our findings reveal barley inflorescence phase-specific gene expression, identify new candidate genes that regulate barley meristem activities and flower development, and provide a new genetic resource for further dissection of the molecular mechanisms of spike development. Keywords: Inflorescence meristem, Transcriptome, Gene expression, Hormones, Barley

In this article, we studied a robust resource constrained flexible project scheduling problem (RRCFPSP), in which the activity duration is an uncertain number and each activity may have multiple alternative execution routes. We represent RRCFPSP by an AND/OR network and propose a mathematical formulation. Moreover, optimal solution can be reached when the duration of each activity takes the maximum value. To employ the heuristic algorithms which specialise in solving continuous problems to solve the considered problem effectively, we develop a novel float weight optimization frame (FWOF). By combining this frame with particle swarm optimization (PSO), gravity search algorithm (GSA) and whale optimization algorithm (WOA), we propose three algorithms FPSO, FGSA and FWOA respectively. Finally, we design a series of numerical experiments, and experimental results show that the FWOF can make traditional certain heuristic algorithms solve the problems we consider effectively and accurately, especially in large-scale cases.

The mesoporous Co/KIT-6 catalysts with 10 wt% Co loading were prepared at different reduction temperatures. The physicochemical properties of the Co/KIT-6 catalysts were characterized by TEM, BET, XRD, XPS, and CO2-TPD, and the effect of reduction temperature on CO2 hydrogenation catalytic performance of Co/KIT-6 catalysts was studied. The results show that reduction temperature has a significant impact on the specific surface area, crystallinity and content of Co0 species, and CO2 adsorption performance of the prepared Co/KIT-6 catalysts. The low reduction temperature is not conduct to the reduction of CoOx, while the high reduction temperature lead to the increase of Co0 species crystallinity and the formation of Co2SiO4 and/or CosbndOsbndSi species which are unfavorable to the form Co0 species. The RT400 catalyst exhibits the highest Co0 species content and the best CO2 adsorption performance. Both the CO2 hydrogenation catalytic activity and CH4 selectivity of the Co/KIT-6 catalysts follow the order: RT400 > RT350 > RT450 > RT500 > RT550, with a small amount of by-product CO. At the reaction temperature of 340 °C, the CO2 reaction rate (CO2 conversion) of the RT400 catalyst can reach 2.98 × 10−5 mol/gcat/s (40%), the CH4 and CO selectivity are 86.7% and 13.3%, respectively.

Vegetable soils are the hotspots of nitric oxide (NO) emissions from agro-ecosystems due to intensive management. Biochar application has been recommended as an important countermeasure, however, its mitigation efficiency is limited as biochar largely stimulates soil nitrification. Therefore, co-applied with nitrification inhibitor could maximize the mitigation potential of biochar. Herein, an incubation experiment was conducted to investigate the effects of biochar and nitrification inhibitor applied alone or in combination on NO fluxes, mineral nitrogen intensity and the populations of nitrifiers and denitrifiers in a typical tropical soil under intensive vegetable cultivation. Results showed that biochar or nitrification inhibitor alone significantly decreased NO emissions by 41.6% and 43.5%, respectively, and their co-applications decreased it by up to 68.5%. Soil NO 2 − −N intensity, along with gene abundances of AOB- amoA and nirK were significantly and positively correlated with cumulative NO emissions. Nitrification inhibitor weakened the stimulating effects of biochar on AOB- amoA and total comammox- amoA gene abundances, as well as NO 3 − −N intensity, thus weakening nitrification-driven NO production. Biochar increased qnorB abundance in soils following nitrification inhibitor addition, thereby promoting NO consumption during denitrification. Moreover, biochar or nitrification inhibitor alone decreased nirK gene abundance and NO 2 − −N intensity and these reductions become greater with their co-applications. The present study highlights that co-applications of biochar and nitrification inhibitor can achieve a higher potential to decrease NO emissions from vegetable soil, which should be further verified in the field considering the complex environment conditions in tropical regions.

Temperature stresses affect plant phenotypic diversity. The developmental stability of the inflorescence, required for reproductive success, is tightly regulated by the interplay of genetic and environmental factors. However, the mechanisms underpinning how plant inflorescence architecture responds to temperature are largely unknown. We demonstrate that the barley SEPALLATA MADS-box protein HvMADS1 is responsible for maintaining an unbranched spike architecture at high temperatures, while the loss-of-function mutant forms a branched inflorescence-like structure. HvMADS1 exhibits increased binding to target promoters via A-tract CArG-box motifs, which change conformation with temperature. Target genes for high-temperature-dependent HvMADS1 activation are predominantly associated with inflorescence differentiation and phytohormone signalling. HvMADS1 directly regulates the cytokinin-degrading enzyme HvCKX3 to integrate temperature response and cytokinin homeostasis, which is required to repress meristem cell cycle/division. Our findings reveal a mechanism by which genetic factors direct plant thermomorphogenesis, extending the recognized role of plant MADS-box proteins in floral development.

The mesoporous Co/SiO2 catalysts with 10 wt.% Co loading were prepared by grind-impregnation method for CO2 hydrogenation, and the mesoporous SiO2 materials with different structures were used as the supports. The physicochemical properties of the Co/SiO2 catalysts were characterized by TEM, BET, H2-TPR, XRD, and CO2-TPD. The results indicate that the pore size, specific surface area, and the Co° species crystallinity of the Co/SiO2 catalyst increase with increasing crystallization temperature of preparing SiO2 support. The Co/SiO2 catalyst has the best CO2 adsorption property when SiO2 support is prepared at the crystallization temperature of 100 °C. The CO2 reaction rate (CO2 conversion) of the Co/SiO2 catalysts is as follows: CK100 > CK080 > CK120 > CK060 > CK140 > CK040, and the CH4 selectivity follows the order: CK100 > CK080 > CK120 > CK140 > CK060 > CK040, accompanied by a certain amount of by-product CO molecules. At 360 °C, the CO2 reaction rate (CO2 conversion) of the CK100 catalyst is up to 3.29 × 10−5 mol/gcat/s (44.3%), the CH4 and CO selectivity are 86.5% and 13.5%, respectively.

The ordered mesoporous Co/KIT-6 catalysts with different Co loading (15–30 wt%) were prepared by impregnation method for CO2 hydrogenation. The physicochemical properties of the catalysts were characterized by H2-TPR, XRD, BET, H2-TPD, and CO2-TPD. The results show that the crystal size of Co species increases with the increasing Co content and the dispersion of Co species reveals an opposite trend. The adsorption capacities of H2 and/or CO2 molecules achieve a maximum value at the Co loading of 25 wt%. The CO2 reaction rate and CH4 selectivity are in the following order: Co15

This paper analyses a type of extension of the resource-constrained project scheduling problem (RCPSP) whose activities have multiple execution lists. This sort of problem is titled resource-constrained project scheduling problem with flexible activity execution lists (abbreviated as RCPSP-FAL). The goal is to minimize makespan and maximize robustness simultaneously to improve the ability of the project to handle emergencies within the design completion time. Six robustness measures are proposed depending on the free slack determined by start time and renewable resource surplus per unit time. Moreover, a bi-objective mixed-integer programming model is developed to solve the considered problem. Besides, a simple data structure, namely selection-tree, is designed to detect available activity execution lists. Two algorithms are proposed, whose names are variant-genetic algorithm (VGA) and variant-simulated-annealing algorithm (VSA), to solve the developed model. Also, an aggregation function is established for assessing the fitness consisting of makespan and robustness. Several computation experiments are designed to evaluate the proposed algorithms and the six robustness measures. Results show the characteristics of different robust measures. Meanwhile, they also show the effectiveness of VSA and VGA, as well as the relations among robustness, makespan, and resource-usage.

The CeO 2 , which were prepared by hard-template method, soft-template method, and precipitation method, were used as support to prepare Ni/CeO 2 catalysts (named as NCT, NCS, and NCP catalysts, respectively). The prepared catalysts were characterized by X-ray diffraction (XRD), transmission electron microscopy (TEM), and Brunauer–Emmett–Teller (BET). Hydrogen temperature-programmed reduction (H 2 -TPR) was also used to study the reducibility of the support nickel precursors. Moreover, CO 2 catalytic hydrogenation methanation was used to investigate the catalytic properties of the prepared NCT, NCS, and NCP catalysts. H 2 -TPR and XRD results showed that the NiO can be reduced by H 2 to produce metal Ni species, and the surface oxygen species existing on the surface of the support CeO 2 can also be reduced by H 2 to form surface oxygen vacancies. Low-angle XRD, TEM, and BET results indicated that the NCT and NCS catalysts had developed mesoporous structure and high specific surface area of 104.7 m 2 g −1 and 53.6 m 2 g −1 , respectively. The NCT catalyst had the highest CO 2 methanation activity among the studied NCT, NCS, and NCP catalysts. The CO 2 conversion and CH 4 selectivity of the NCT catalyst can reach 91.1% and 100% at 360 °C and atmospheric pressure. The NCP catalyst, which had low specific surface area and low porosity, performed less CO 2 conversion and higher CH 4 selectivity than the NCT and NCS catalysts till 400 °C.

Compared to earlier mobile network generations, the 5G system architecture has been significantly enhanced by the introduction of network analytics functionalities and ex- tended capabilities of interacting with third party Application Functions (AFs). Combining these capabilities, new features for Quality of Experience (QoE) estimation can be designed and introduced in next generation networks. It is, however, unclear how 5G networks can collect monitoring data and application metrics, how they correlate to each other, and which techniques can be used in 5G systems for QoE estimation. This paper studies the feasibility of Machine Learning (ML) techniques for QoE estimation and evaluates their performance for a mobile video streaming use-case. A simulator has been implemented with OMNeT++ for generating traces to (i) examine the relevance of features generated from 5G monitoring data and (ii) to study the QoE estimation accuracy (iii) for a variable number of used features. © ACM, 2019. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published here, https://doi.org/10.1145/3349611.3355547

CeO2, which was used as support to prepare mesoporous Ni/CeO2 catalyst, was prepared by the hard-template method. The prepared NiO/CeO2 precursor and Ni/CeO2 catalyst were studied by H2–TPR, in-situ XPS, and in-situ FT-IR. The catalytic properties of the prepared Ni/CeO2 catalyst were also investigated by CO2 catalytic hydrogenation methanation. H2–TPR and in-situ XPS results showed that metal Ni species and surface oxygen vacancies could be formed by H2 reduction. In-situ FT-IR and in-situ XPS results indicated that CO2 molecules could be reduced by active metal Ni species and surface oxygen vacancies to generate active CO species and promote CO2 methanation. The Ni/CeO2 catalyst presented the high CO2 methanation activity, and CO2 conversion and CH4 selectivity reached 91.1% and 100% at 340 °C and atmospheric pressure.

The research of the existing screening algorithm for speech frame has a great deal of shortcomings, such as its applicability and complexity. Worse still, those frames which are screened by algorithm can't achieve the requirement of express and the screening process greatly damages the original signal. This paper presents a strategy for screening speech keyframe and designs a screening scheme for key-frames based on the strategy. This scheme refers to the speech's logarithm energy and the weighted-zero-crossing rate. The detail process of screening scheme: firstly, screening frames according to the logarithm energy of the speech signals. Meanwhile, combine the speech amplitude and zero-crossing rate for frames screening. Finally, calculate the similarity of the two screening results, and getting the key-frame set. In addition, the scheme has been analyzed and validated from those aspects like continuity, characterization and applicability, in order to verify the effectiveness and availability. The results illustrate that these frames which have been screened by this scheme have advantages of continuity, characterization etc.

Friction and wear experiments were carried out with nanoscale serpentine (magnesium silicate mineral) and magnesium hexasilicate powder as lubricating oil additives at 400 °C. The tribological test results showed that the self-repairing protective layers could be well formed on the contact surfaces, whether nanoscale serpentine or magnesium hexasilicate powder was added into lubricating oil. The SEM and EDAX analysis demonstrate that the morphology and elements of both self-repairing layers are in accordance with each other. The research results indicate that the essence of self-repairing of serpentine power is isomorphic replacement and tribochemical reaction between magnesium silicate and metal matrix.

Regional disparities in geographical access to hospital care are found throughout China. Understanding variations in the spatial accessibility of hospital care has the potential to provide decision support in healthcare planning. This study examines the hospital system in the Sichuan Province in China, which provides healthcare for more than 80 million people. We examine the impacts of accessibility characterisation via the conventional measurement approach by comparing the results to those derived using a floating catchment area approach. Employing a geographical information system based on population and hospital administrative data, we conducted a provincewide study of the spatial accessibility of hospital care in Sichuan Province, China. A shortest-path analysis and the enhanced two-step floating catchment area (E2SFCA) method were implemented. Substantial differences between these two approaches were found, including a roughly 15% difference in the total number of under-served areas. Generally, spatial accessibility was higher in the eastern regions of Sichuan. More than 5.5 million people were found to have limited access, with large variations across the province. These results indicate that the official method used by policy makers in China may not capture the true nature of spatial accessibility throughout the region. We recommend that the E2SFCA method be implemented for health services research in China, providing decision makers with more accurate information when setting healthcare policies.

Clinical Pathway (CP) is very complicated and has many exceptional variations. Generally, its treatment course and control steps can not be totally predefined. Meanwhile, the CP embodies the “Re-flow” therapy features, which is very hard to model, control and manage. Therefore, combined modular modelling method and structure changing mechanisms, a Modular Colored Petri Net with changeable structure (MCPN-CS) workflow modelling method is proposed. A special type of transition called main bus gate (MBG) is introduced to solve the “Re-flow” problem. Moreover, aimed at the variations of the CP, the workflow model for the CP can be reconfigured dynamically by using the mechanism of change-by-modification (CBM) and change-by-composition (CBC). A case study on the osteosarcoma CP evolution workflow modelling is constructed and the modelling is analyzed by presented deadlock detection algorithms (DDA). The result validates that the proposed method can noticeably enhance the flexibility, adaptation, reusability and maintainability workflow model for the CP.

Implementing efficient scheduling and dispatching policies is a critical means to gain competitiveness for modern semiconductor manufacturing systems. In contemporary global market, a successful semiconductor manufacturer has to excel in multiple performance indices, consequently qualified scheduling approaches should provide efficient and holistic management of wafer products, information and manufacturing resources and make adaptive decisions based on real-time processing status to reach an overall optimized system performance. To cope with this challenge, a timed extended object-oriented Petri nets (EOPNs) based multiple-objective scheduling and real-time dispatching approach is proposed in this paper. Four performance objectives pursued by semiconductor manufacturers are integrated into a priority-ranking algorithm that serves as the initial scheduling guidance, and then all wafer lots will be dynamically dispatched by the hybrid real-time dispatching control system. A set of simulation experiments validate the proposed multiple-objective scheduling and real-time dispatching algorithm may achieve satisfactory performances.

Semiconductor wafer fabrication system (SWFS) is the most complex and capital-intensive phase in the entire semiconductor manufacturing cycle. With characteristics of re-entrant processing routes, equipment uncertainty, product diversity and improving technologies, great challenges are presented in SWFS's modeling, scheduling and simulation. To implement efficient production control, this paper provides a timed extended object-oriented Petri nets (TEOPNs) approach to performance modeling, real-timed dispatching and simulation of SWFSs. The TEOPNs models are constructed in a hierarchy to accord with the real-world SWFS's organization, and a new type of signal place is added into the TEOPNs to respond the dynamic states of all processing facilities. A novel autonomy and coordination-based real-time dispatching mechanism (A&C-RDM) is developed in this paper, which executes under the support of the TEOPNs-based hybrid real-time dispatching control system (HRDCS). Owning to the ability of gathering dynamic real-time information of all production facilities and WIP products, the HRDCS can make adaptive dispatching decisions according to the local and global real-time processing status. Two sets of key elements of real-time dispatching, i.e. the state thresholds and dispatching rules, are defined in the HRDCS so that the A&C-RDM can integrate different types of dispatching rules. A set of simulation experiments prove the efficiency of the proposed modeling and dispatching algorithm. In summary, the proposed TEOPNs, HRDCS and A&C-RDM form the cornerstones of a real-time dispatching simulation prototype of SWFS, and the work described in this paper carries out an advanced integrated ''modeling-dispatching-simulation'' methodology.

A self-designed new robot system to be employed in the weld inspection of nuclear pipes is introduced in this paper. This robot system can effectively serve as a substitute for a worker who would otherwise be exposed to a hazardous environment. The robot system includes a half clamp as a mounting unit and a half base as circumferential rotation joint. This system can be mounted on a branch pipe at any position. Its arm is designed with six joints for controlling the position and orientation of the ultrasonic probe (USP), which serves as an end-effector. All of these seven joints, which yield seven degrees of freedom (DOF), are driven by stepper motors. The communication between the Industrial Personal Computer (IPC) and Micro Chip Unit (MCU) is based on RS-485, which allows the robot system to be operated and monitored away from a radiative environment and can therefore limit radiation exposure for the operator. Taking the application at different working sites in nuclear power stations into consideration, the design of the system is based on a modular structure, and the substitution of the clamp and base, as well as the adjustment of the size of the arm links are taken into account. Finally, a test unit for the designed robot system is set up, and experiments show that this system could meet the requirements of a real nuclear power station environment.

In this paper, extended object-oriented Petri nets (EOPNs) are proposed for the effective modelling of semiconductor wafer fabrication systems (SWFSs). To cope with their complexity in terms of the re-entrant process route and the mixed production mode, a special type of transition called main-bus gate is introduced, which may lead each kind of product to undergo every re-entrant processing stage. In addition, the hierarchical approach is also applied to cope with the complexity. An etching area that processes 0.25 µm logic IC products is taken as an illustration to present the detailed modelling procedures by EOPNs, and the resulting model validates that the EOPNs may cope well with complex SWFSs modelling.

In this paper we propose an extended object-oriented Petri nets (EOPNs) for effective modeling of large-scale complex re-entrant manufacturing systems (ReMSs). To cope with their complexity because of the re-entrant process route and the mixed production mode, a special type of transition called a main-bus gate (MBG) is introduced, which can cause products to undergo every re-entrant manufacturing stage. A hierarchical approach is also applied to cope with the complexity. As a typical large-scale complex ReMS, the semiconductor wafer fabrication system (SWFS) is taken as an example for demonstrating the proposed EOPNs. A case study is provided to show the modelling procedures, and the resulting model validates that the EOPNs can cope well with the modelling complexity of large-scale complex ReMSs.

Real-time scheduling of semiconductor wafer fabrication system (SWFS) is complicated due to the following factors. re-entrant product flow, high uncertainties in operations and rapidly changing products and technologies. This paper presents a simulation platform for real-time scheduling of SWFS, which is designed and developed based on extended object-oriented petri net (EOPN) to describe the complicated wafer fabrication process and serve as the foundation of real-time scheduling. In addition, a dynamic bottleneck dispacthing (DBD) algorithm is designed to detect bottlenecks in a timely way and make adaptive dispatching decisions according to the real-time conditions. The results of the simulation experiments and analysis show that the DBD algorithm is superior to CR+FIFO and EDD methods.

This paper presents a multiple-objective real-time scheduler for semiconductor wafer fab using Colored Timed Object-oriented Petri net (CTOPN). First, a high-fidelity hierarchical CTOPN model of wafer fab is constructed, upon which a hierarchical control system is also formed. To rank WIP lots in meeting multiple scheduling objectives, a combined priority index is then developed in the fab level controller. To cope with unpredictable events occurred on the equipments, such as breakdown, real-time dispatching policies are encapsulated in the work station-level controllers, which dynamically dispatch wafer lot, and the dispatching results are fed back to fab controller through work area controllers.

Modern microelectronics manufacturing may be described by re-entrant manufacturing systems (ReMS). To be competitive, ReMS are designed to run fully automated and effective production control and schedule policies are pursued, however, a high-fidelity system model is the prerequisite. In this paper, an extended object-oriented Petri nets (EOPN) is proposed to cope with the complexity of re-entrant process routes and mixed production modes. To ensure the resulting EOPN model is deadlock free, an algorithm is introduced to analyze the resulting EOPN model so that all possible deadlock situations may be detected and avoided before any control and schedule policy is implemented. In addition, formal description of the related conflicts and resolving algorithm are also presented in this paper.