Single machine scheduling with release dates and job delivery to minimize the makespan

AbstractIn single machine scheduling with release dates and job delivery, jobs are processed on a single machine and then delivered by a capacitated vehicle to a single customer. Only one vehicle is employed to deliver these jobs. The vehicle can deliver at most c jobs at a shipment. The delivery completion time of a job is defined as the time at which the delivery batch containing the job is delivered to the customer and the vehicle returns to the machine. The objective is to minimize the makespan, i.e., the maximum delivery completion time of the jobs. When preemption is allowed to all jobs, we give a polynomial-time algorithm for this problem. When preemption is not allowed, we show that this problem is strongly NP-hard for each fixed c≥1. We also provide a 53-approximation algorithm for this problem, and the bound is tight

Protocol selection for second-order consensus against disturbance

Noticing that both the absolute and relative velocity protocols can solve the second-order consensus of multi-agent systems, this paper aims to investigate which of the above two protocols has better anti-disturbance capability, in which the anti-disturbance capability is measured by the L2 gain from the disturbance to the consensus error. More specifically, by the orthogonal transformation technique, the analytic expression of the L2 gain of the second-order multi-agent system with absolute velocity protocol is firstly derived, followed by the counterpart with relative velocity protocol. It is shown that both the L2 gains for absolute and relative velocity protocols are determined only by the minimum non-zero eigenvalue of Laplacian matrix and the tunable gains of the state and velocity. Then, we establish the graph conditions to tell which protocol has better anti-disturbance capability. Moreover, we propose a two-step scheme to improve the anti-disturbance capability of second-order multi-agent systems. Finally, simulations are given to illustrate the effectiveness of our findings

Uncertainty-Aware Unlikelihood Learning Improves Generative Aspect Sentiment Quad Prediction

Recently, aspect sentiment quad prediction has received widespread attention in the field of aspect-based sentiment analysis. Existing studies extract quadruplets via pre-trained generative language models to paraphrase the original sentence into a templated target sequence. However, previous works only focus on what to generate but ignore what not to generate. We argue that considering the negative samples also leads to potential benefits. In this work, we propose a template-agnostic method to control the token-level generation, which boosts original learning and reduces mistakes simultaneously. Specifically, we introduce Monte Carlo dropout to understand the built-in uncertainty of pre-trained language models, acquiring the noises and errors. We further propose marginalized unlikelihood learning to suppress the uncertainty-aware mistake tokens. Finally, we introduce minimization entropy to balance the effects of marginalized unlikelihood learning. Extensive experiments on four public datasets demonstrate the effectiveness of our approach on various generation templates1

Aqueous electrosynthesis of an electrochromic material based water-soluble EDOT-MeNH2 hydrochloride

2\u27-Aminomethyl-3,4-ethylenedioxythiophene (EDOT-MeNH2) showed unsatisfactory results when its polymerization occurred in organic solvent in our previous report. Therefore, a water-soluble EDOT derivative was designed by using hydrochloric modified EDOT-MeNH2 (EDOT-MeNH2·HCl) and electropolymerized in aqueous solution to form the corresponding polymer with excellent electrochromic properties. Moreover, the polymer was systematically explored, including electrochemical, optical properties and structure characterization. Cyclic voltammetry showed low oxidation potential of EDOT-MeNH2·HCl (0.85 V) in aqueous solution, leading to the facile electrodeposition of uniform the polymer film with outstanding electroactivity. Compared with poly(2′-aminomethyl- 3,4-ethylenedioxythiophene) (PEDOT-MeNH2), poly(2′-aminomethyl-3,4-ethylenedioxythiophene salt) (PEDOT-MeNH3 +A-) revealed higher efficiencies (156 cm2 C-1), lower bandgap (1.68 eV), and faster response time (1.4 s). Satisfactory results implied that salinization can not only change the polymerization system, but also adjust the optical absorption, thereby increase the electrochromic properties

Distributions of dissolved inorganic carbon and total alkalinity in the Western Arctic Ocean

The third Chinese National Arctic Research Expedition (3rd CHINARE-Arctic in 2008) was carried out from July to September 2008. During the survey, numerous sea water samples were taken for CO2 parameter measurement (including total alkalinity TA and total dissolved inorganic carbon DIC).The distribution of CO2 parameters in the Western Arctic Ocean was determined, and the controlling factors are addressed. The ranges of summertime TA, normalized TA (nTA), DIC and normalized DIC (nDIC) in the surface seawater were 1.757 - 2.229 μmol * kg(-1), 2.383 - 2.722 μmol * kg(-1), 1.681 - 2.034 μmol * kg(-1), 2.119 - 2.600 μmol * kg(-1), respectively. Because of dilution from ice meltwater, the surface TA and DIC concentrations were relatively low. TA in the upper 100 m to the south of 78±N had good correlation with salinity, showing a conservative behavior. The distribution followed the seawater-river mixing line at salinity >30, then followed the seawater mixing line (diluted by river water to salinity = 30) with the ice meltwater. The DIC distribution in the Chukchi Sea was dominated by biological production or respiration of organic matter, whereas conservative mixing dominated the mixed layer TA distribution in the ice-free Canada Basin

PSSP-RFE: Accurate Prediction of Protein Structural Class by Recursive Feature Extraction from PSI-BLAST Profile, Physical-Chemical Property and Functional Annotations

Protein structure prediction is critical to functional annotation of the massively accumulated biological sequences, which prompts an imperative need for the development of high-throughput technologies. As a first and key step in protein structure prediction, protein structural class prediction becomes an increasingly challenging task. Amongst most homological-based approaches, the accuracies of protein structural class prediction are sufficiently high for high similarity datasets, but still far from being satisfactory for low similarity datasets, i.e., below 40% in pairwise sequence similarity. Therefore, we present a novel method for accurate and reliable protein structural class prediction for both high and low similarity datasets. This method is based on Support Vector Machine (SVM) in conjunction with integrated features from position-specific score matrix (PSSM), PROFEAT and Gene Ontology (GO). A feature selection approach, SVM-RFE, is also used to rank the integrated feature vectors through recursively removing the feature with the lowest ranking score. The definitive top features selected by SVM-RFE are input into the SVM engines to predict the structural class of a query protein. To validate our method, jackknife tests were applied to seven widely used benchmark datasets, reaching overall accuracies between 84.61% and 99.79%, which are significantly higher than those achieved by state-of-the-art tools. These results suggest that our method could serve as an accurate and cost-effective alternative to existing methods in protein structural classification, especially for low similarity datasets

Marine biogenic aerosols and their effects on aerosol-cloud interactions over the Southern Ocean: a review

The Southern Ocean (SO) plays an important role in the global climate system. Changes in SO biogeochemistry and marine ecosystems may influence the distribution of atmospheric aerosols and clouds and impact the climate system. We reviewed current knowledge on the interactions between marine aerosols and clouds over the SO. We focused on marine primary and secondary organic aerosols and summarized their characteristics, processes and roles as cloud condensation nuclei and ice nuclei. We described in detail the interactions between the marine ecosystem, aerosols and clouds. We discussed marine productivity, formation of marine biogenic aerosols and interactions between aerosols, clouds and climate. We explored the impact of climate change on SO marine ecosystem productivity and aerosol–cloud–climate feedback. Marine biogenic aerosols could impact the radiation budget and oceanic low-level clouds over the SO. This study contributes towards an improved understanding of marine productivity, aerosol-cloud interactions and climate change in the SO. The SO may respond to climate change in varying degrees. More studies are urgently needed to support accurate forecasts of future changes in the SO

Summertime freshwater fractions in the surface water of the western Arctic Ocean evaluated from total alkalinity

As a quasi-conservative tracer, measures of total alkalinity (TA) can be utilized to trace the relative fractions of freshwater and seawater. In this study, based on the TA and related data collected during the third Chinese National Arctic Research Expedition (July—September 2008, 3rd CHINARE-Arctic) and the fourth Chinese National Arctic Research Expedition (July — September 2010, 4th CHINARE-Arctic), fractions of sea-ice meltwater, river runoff, and seawater within the surface water of the western Arctic Ocean were determined using salinity and TA relationships. The largest fraction of sea-ice meltwater was found around 75°N within the Canada Basin during both surveys, which is located at the ice edge. Generally, it was found that the fraction of river runoff was less than that of sea-ice meltwater. The river runoff, composed mainly of contributions from the Yukon River carried by Bering inflow water and the Mackenzie River, was influenced by the currents, leading to two peak areas of its fraction. Our results show that the dilution effect of freshwater carried by Bering inflow water during the 3rd CHINARE-Arctic in 2008 expedition period may be stronger than that during the 4th CHINARE-Arctic in 2010 expedition period. The peak area of sea-ice meltwater fraction during the 4th CHINARE-Arctic was different from that of the 3rd CHINARE-Arctic, corresponding to their sea-ice condition

Characteristics of metals in the aerosols of Zhongshan Station, Antarctica

Instrumental neutron activation analysis(INAA) was applied to analyze the bulk, high-volume aerosol samples, collected at Zhongshan Station in the Eastern Antarctica, during 1998-2001, to study the chemical species. A graphical technique was applied to the INAA data. Results showed that Na, Cl, Mg, Ca, Sr, Br, I, Sr and Rb were marine elements while Al, Sc, Fe and Mn were crustal elements. Compared to marine and crustal elements, five elements(Se, Co, Sb, Zn, Cr) were highly abundant in the aerosols collected at Zhongshan station, which indicated that they might come from the petroleum burning, heating and equipment operation. The presence of pollutant elements suggested that human activities have affected the local environments in Antarctica