A capacitated vehicle routing problem with order available time in e-commerce industry

<p>In this article, a variant of the well-known capacitated vehicle routing problem (CVRP) called the capacitated vehicle routing problem with order available time (CVRPOAT) is considered, which is observed in the operations of the current e-commerce industry. In this problem, the orders are not available for delivery at the beginning of the planning period. CVRPOAT takes all the assumptions of CVRP, except the order available time, which is determined by the precedent order picking and packing stage in the warehouse of the online grocer. The objective is to minimize the sum of vehicle completion times. An efficient tabu search algorithm is presented to tackle the problem. Moreover, a Lagrangian relaxation algorithm is developed to obtain the lower bounds of reasonably sized problems. Based on the test instances derived from benchmark data, the proposed tabu search algorithm is compared with a published related genetic algorithm, as well as the derived lower bounds. Also, the tabu search algorithm is compared with the current operation strategy of the online grocer. Computational results indicate that the gap between the lower bounds and the results of the tabu search algorithm is small and the tabu search algorithm is superior to the genetic algorithm. Moreover, the CVRPOAT formulation together with the tabu search algorithm performs much better than the current operation strategy of the online grocer.</p

Sequential sampling of visual objects during sustained attention

<div><p>In a crowded visual scene, attention must be distributed efficiently and flexibly over time and space to accommodate different contexts. It is well established that selective attention enhances the corresponding neural responses, presumably implying that attention would persistently dwell on the task-relevant item. Meanwhile, recent studies, mostly in divided attentional contexts, suggest that attention does not remain stationary but samples objects alternately over time, suggesting a rhythmic view of attention. However, it remains unknown whether the dynamic mechanism essentially mediates attentional processes at a general level. Importantly, there is also a complete lack of direct neural evidence reflecting whether and how the brain rhythmically samples multiple visual objects during stimulus processing. To address these issues, in this study, we employed electroencephalography (EEG) and a temporal response function (TRF) approach, which can dissociate responses that exclusively represent a single object from the overall neuronal activity, to examine the spatiotemporal characteristics of attention in various attentional contexts. First, attention, which is characterized by inhibitory alpha-band (approximately 10 Hz) activity in TRFs, switches between attended and unattended objects every approximately 200 ms, suggesting a sequential sampling even when attention is required to mostly stay on the attended object. Second, the attentional spatiotemporal pattern is modulated by the task context, such that alpha-mediated switching becomes increasingly prominent as the task requires a more uniform distribution of attention. Finally, the switching pattern correlates with attentional behavioral performance. Our work provides direct neural evidence supporting a generally central role of temporal organization mechanism in attention, such that multiple objects are sequentially sorted according to their priority in attentional contexts. The results suggest that selective attention, in addition to the classically posited attentional “focus,” involves a dynamic mechanism for monitoring all objects outside of the focus. Our findings also suggest that attention implements a space (object)-to-time transformation by acting as a series of concatenating attentional chunks that operate on 1 object at a time.</p></div

DataSheet_1_MiR-199a-3p-regulated alveolar macrophage-derived secretory autophagosomes exacerbate lipopolysaccharide-induced acute respiratory distress syndrome.pdf

PurposeAcute respiratory distress syndrome (ARDS) is a prevalent illness in intensive care units. Extracellular vesicles and particles released from activated alveolar macrophages (AMs) assist in ARDS lung injury and the inflammatory process through mechanisms that are unclear. This study investigated the role of AM-derived secretory autophagosomes (SAPs) in lung injury and microRNA (MiR)-199a-3p-regulated inflammation associated with ARDS in vitro and in a murine model.MethodsThe ARDS model in mouse was established by intratracheal LPS lipopolysaccharide (LPS) injection. The agomirs or antagomirs of MiR-199a-3p were injected into the caudal vein to figure out whether MiR-199a-3p could influence ARDS inflammation and lung injury, whereas the mimics or inhibitors of MiR-199a-3p, siRNA of Rab8a, or PAK4 inhibitor were transfected or applied to RAW264.7 cells to evaluate the mechanism of SAP release. Culture supernatants of RAW264.7 cells treated with LPS or bronchoalveolar lavage fluid from mice were collected for the isolation of SAPs.ResultsWe found that MiR-199a-3p was over-expressed in the lungs of ARDS mice. The MiR-199a-3p antagomir alleviated, whereas the MiR-199a-3p agomir exacerbated LPS-induced inflammation in mice by promoting AM-derived SAP secretion. In addition, MiR-199a-3p over-expression exacerbated LPS-induced ARDS via activating Rab8a, and Rab8a silencing significantly suppressed the promoting influence of the MiR-199a-3p mimic on SAP secretion. Furthermore, MiR-199a-3p mimic activated Rab8a by directly inhibiting PAK4 expression.ConclusionThe novel finding of this study is that MiR-199a-3p participated in the regulation of SAP secretion and the inflammatory process via targeting of PAK4/Rab8a, and is a potential therapeutic candidate for ARDS treatment.</p

Nanopore Creation in Graphene by Ion Beam Irradiation: Geometry, Quality, and Efficiency

Ion beam irradiation is a promising approach to fabricate nanoporous graphene for various applications, including DNA sequencing, water desalination, and phase separation. Further advancement of this approach and rational design of experiments all require improved mechanistic understanding of the physical drilling process. Here, we demonstrate that, by using oblique ion beam irradiation, the nanopore family is significantly expanded to include more types of nanopores of tunable geometries. With the hopping, sweeping, and shoving mechanisms, ions sputter carbon atoms even outside the ion impact zone, leading to extended damage particularly at smaller incident angles. Moreover, with lower energies, ions may be absorbed to form complex ion-carbon structures, making the graphene warped or curly at pore edges. Considering both efficiency and quality, the optimal ion energy is identified to be 1000 eV at an incident angle of 30° with respect to the graphene sheet and 400–500 eV at higher incident angles. All of these results suggest the use of oblique ion beam and moderate energy levels to efficiently fabricate high-quality nanopores of tunable geometries in graphene for a wide range of applications

Experiment 3 (50% cue validity) and Experiment 4 (100% cue validity, multiple object tracking [MOT]).

<p>(A) In Experiment 3 (50% cue validity), subjects fixated on a central point and covertly attended to 2 discs presented in the left and right visual fields for target detection. Subjects were instructed to simultaneously pay attention to both discs and were informed that the target would be equally likely to appear within the discs and that the initial cue would not predict the target location. After a noninformative red circle cue (cue validity: 50%) appeared around 1 of the 2 discs, the luminance of the 2 discs was independently and randomly modulated for 5 s (top: cued visual sequence; bottom: uncued visual sequence), during which time subjects were instructed to monitor a randomly occurring target. (B) Grand average (<i>N</i> = 16) time–frequency plots for cued–uncued TRF power difference in Experiment 3 (cue validity: 50%). Note the prolonged alpha-band switching (blue–red pattern), suggesting that attentional shifting is enhanced when attention is evenly distributed across the 2 spatial locations (50% cue validity). (C) In Experiment 4 (MOT experiment), a red circle cue at the beginning of each trial indicated which disc the subjects should covertly attend to for subsequent target detection. The 2 disks were then moved randomly and smoothly across the screen for 5 s, during which time the subjects were instructed to detect the appearance of a target within the cued disc. Here, the cue validity was 100%, which means that the target only appeared in the cued disk, similar to Experiment 1. (D) Experiment 4 results. Top: Grand average (<i>N</i> = 11) time–frequency plots for att–unatt TRF power difference. Bottom: grand average (blue lines, <i>N</i> = 11, mean ± SEM) time course for att–unatt TRF power within the alpha band (8–12 Hz). Red horizontal lines at the bottom indicate points showing significant power differences in the alpha band between att and unatt (<i>p</i> < 0.05, one-tailed, false discovery rate [FDR] corrected). Note the initial alpha inhibition followed by an alpha rebound trend, similar to Experiment 1 (see <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2001903#pbio.2001903.g002" target="_blank">Fig 2C</a>). The data are provided in the Supporting Information (see <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2001903#pbio.2001903.s008" target="_blank">S3 Data</a>).</p

Alpha inhibition-rebound effects relate to attentional behavior.

<p>Correlations between behavioral (behavioral index, BI) and alpha switching (neuronal index, NI) measures across participants (Experiment 2: <i>n</i> = 20, disc; Experiment 3: <i>n</i> = 16, circle). BI: Contrast_unatt−Contrast_att; NI: alpha_reb−alpha_inh. The negative correlations indicate that, as the unattended object obtained more attentional behavioral benefit (smaller BI), the alpha rebound effects became stronger (larger NI). The data are provided in the Supporting Information (see <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2001903#pbio.2001903.s009" target="_blank">S4 Data</a>).</p

Experimental paradigm for Experiments 1 and 2 and illustration of the temporal response function (TRF) approach.

<p>(A) A central arrow cue appeared at the beginning of each trial to indicate which side (left or right) the subject should covertly attend to for subsequent target detection. Two discs were then presented simultaneously in the left and right visual fields for 5 seconds, during which time subjects were instructed to detect the appearance of a target square within the discs by pressing 1 of 2 response keys at the end of each trial. The target occurred at a random time so that subjects had to maintain their attention on the discs. Across trials, the contrast of the target square relative to the momentary disc luminance was adjusted to maintain 80% detection performance. For 100% cue validity (Experiment 1), the target only appeared in the cued disc; for 75% cue validity (Experiment 2), the target appeared in the cued disc 75% of the time and in the uncued disc 25% of the time. (B) The luminance of the 2 discs was independently and randomly modulated throughout the trial, resulting in 2 independent 5 s random temporal sequences (example sequences are shown; top: attended visual stimulus luminance sequence, bottom: unattended visual stimulus luminance sequence). At the same time, electroencephalography (EEG) responses were recorded. (C) The TRF approach was used to calculate the impulse brain response for the attended (top, att) and unattended (bottom, unatt) visual sequences. TRF characterizes the brain response to a unit increase in luminance in a stimulus sequence, with the time axis representing the latency after each transient unit. Note that the att TRF and unatt TRF were derived from the same EEG responses but were separated based on the corresponding stimulus luminance sequence (see panel B).</p

Results for Experiment 2 (75% cue validity) and summary of alpha inhibition and alpha rebound.

<p>(A) Temporal response function (TRF) results for Experiment 2 (75% cue validity). Left: grand average (<i>N</i> = 20) time–frequency plots for att–unatt TRF power difference. Right: grand average (blue lines, <i>N</i> = 20, mean ± SEM) time course for att–unatt TRF power within the alpha band (8–12 Hz). Red horizontal lines at the bottom indicate points showing a significant power difference in the alpha band between att and unatt (<i>p</i> < 0.05, two-tailed, false discovery rate [FDR] corrected). Note the emergence of alpha-band rebound right after the alpha-band inhibition, suggesting attentional switching. (B) Grand average att–unatt TRF alpha-band power averaged over an early (alpha inhibition, 0.07–0.11 s, red dotted box in Fig 3A) and a subsequent late (alpha rebound, 0.24–0.28 s, black dotted box in Fig 3A) time range for each experiment. Experiment 1: <i>N</i> = 18; Experiment 2: <i>N</i> = 20; Experiment 3: <i>N</i> = 16; Experiment 4: <i>N</i> = 11. * <i>p</i> < 0.05, ** <i>p</i> < 0.01, <i>t</i> test. MOT: multiple object tracking. The data are provided in the Supporting Information (see <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2001903#pbio.2001903.s007" target="_blank">S2 Data</a>).</p

The Theoretical Construction of a Classification of Clinical Somatic Symptoms in Psychosomatic Medicine Theory

<div><p>Objective</p><p>This article adopts the perspective of psychosomatic medicine to present and test a theoretical model of the classification of clinical somatic symptoms. The theoretical model consists of four dimensions: emotional somatic symptoms, biological somatic symptoms, imaginative somatic symptoms, and cognitive somatic symptoms.</p><p>Method</p><p>A clinical somatic symptom classification scale was developed according to the theoretical model. A total of 542 participants completed the clinical somatic symptoms classification scale. The data were analyzed using exploratory and confirmatory factor analyses.</p><p>Results</p><p>The results confirmed the theoretical model. The analyses found that the proposed theoretical structure of the scale was good, as indicated by factor loadings and fit indices, and that the scale had good reliability and construct validity.</p><p>Conclusions</p><p>Based on the interpretation of the clinical symptoms of psychosomatic medicine, the treatment of chronic non-infectious diseases includes at least three dimensions: the first is the etiological treatment, the second is the pathophysiological and pathopsychological dimension, and the third is symptomatic treatment. The unified psychosomatic point of view and diverse clinical thinking modes are aimed at identifying different classes of somatic symptoms and important prerequisites for the treatment of these symptoms. We registered the study with the Chinese Clinical Trial Registry and it was approved by the West China Hospital, Sichuan University ethics committee. Trial registration: The registration number is ChiCTR-OCS-14004632 (time: 2014-05-12).</p></div

Experiment 2: L-TRF and F-TRF responses when global form property was task irrelevant.

<p>(A) L-TRF responses. (B) F-TRF responses. Upper panel: Grand average (<i>n</i> = 16) plots for TRF waveforms (summarized as root-mean-square across all MEG channels) as a function of temporal lag (−100 to 400 ms). Gray shades indicate the confidence interval after permutation test (see details in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2003646#pbio.2003646.s001" target="_blank">S1 Text</a>). Error bar indicates standard error. Middle panel: Grand average (<i>n</i> = 16) plots for sensor-level topographical distribution for TRF responses. Lower panel: Grand average (<i>n</i> = 16) plots for source localization results in the normalized MNI template (cluster-level permutation test across space and time, multiple comparison corrected, cluster <i>p</i> < 0.05). Note that L-TRF responses showed a similar feedforward profile that started from EVA as that in Experiment 1 (A). Crucially, the IPS-V3a-V1 activation sequence still emerged although was temporally delayed (B). Moreover, the VAN (orange) and DMN (blue) were also activated. The MNI coordinates for all the significant source clusters are listed in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2003646#pbio.2003646.s003" target="_blank">S1 Table</a>. The data underlying Fig 4 can be found in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2003646#pbio.2003646.s005" target="_blank">S1 Data</a>. DMN, default mode network; F-TRF, form coherence TRF; IPS, intraparietal sulcus; L-TRF, luminance TRF; MEG, magnetoencephalography; MNI, Montreal Neurological Institute; ROI, region of interest; TRF, temporal response function; VAN, ventral attention network.</p