A dynamic programming heuristic for vehicle routing with time-dependent travel times and required breaks

For the intensively studied vehicle routing problem (VRP), two real-life restrictions have received only minor attention in the VRP-literature: traffic congestion and driving hours regulations. Traffic congestion causes late arrivals at customers and long travel times resulting in large transport costs. To account for traffic congestion, time-dependent travel times should be considered when constructing vehicle routes. Next, driving hours regulations, which restrict the available driving and working times for truck drivers, must be respected. Since violations are severely fined, also driving hours regulations should be considered when constructing vehicle routes, even more in combination with congestion problems. The objective of this paper is to develop a solution method for the VRP with time windows (VRPTW), time-dependent travel times, and driving hours regulations. The major difficulty of this VRPTW extension is to optimize each vehicle’s departure times to minimize the duty time of each driver. Having compact duty times leads to cost savings. However, obtaining compact duty times is much harder when time-dependent travel times and driving hours regulations are considered. We propose a restricted dynamic programming (DP) heuristic for constructing the vehicle routes, and an efficient heuristic for optimizing the vehicle’s departure times for each (partial) vehicle route, such that the complete solution algorithm runs in polynomial time. Computational experiments demonstrate the trade-off between travel distance minimization and duty time minimization, and illustrate the cost savings of extending the depot opening hours such that traveling before the morning peak and after the evening peak becomes possible.     

Brain-like Intelligent Decision-making Based on Basal Ganglia and Its Application in Automatic Car-following

The anthropomorphic intelligence of autonomous driving has been a research hotspot in the world.However,current stud-ies have not been able to reveal the mechanism of drivers'natural driving behaviors.Therefore,this thesis starts from the perspective of cognitive decision-making in the human brain,which is inspired by the regulation of dopamine feedback in the basal ganglia,and a reinforcement learning model is established to solve the brain-like intelligent decision-making problems in the process of interacting with the environment.In this thesis,first,a detailed bionic mechanism architecture based on basal ganglia was proposed by the consideration and analysis of its feedback regulation mechanism;second,the above mechanism was transformed into a reinforcement Q-learning model,so as to implement the learning and adaptation abilities of an intelligent vehicle for brain-like intelligent decision-making during car-following;finally,the feasibility and effectiveness of the proposed method were verified by the simulations and real vehicle tests.     

The research of prediction model on intelligent vehicle based on driver’s perception

In the field of self-driving technology, the stability and comfort of the intelligent vehicle are the focus of attention. The paper applies cognitive psychology theory to the research of driving behavior and analyzes the behavior mechanism about the driver’s operation. Through applying the theory of hierarchical analysis, we take the safety and comfort of intelligent vehicle as the breakthrough point. And then we took the data of human drivers’ perception behavior as the training set and did regression analysis using the method of regression analysis of machine learning according to the charts of the vehicle speed and the visual field, the vehicle speed and the gaze point as well as the vehicle speed and the dynamic vision. At last we established linear and nonlinear regression models (including the logarithmic model) for the training set. The change in thinking is the first novelty of this paper. Last but not least important, we verified the accuracy of the model through the comparison of different regression analysis. Eventually, it turned out that using logarithmic relationship to express the relationship between the vehicle speed and the visual field, the vehicle speed and the gaze point as well as the vehicle speed and the dynamic vision is better than other models. In the aspect of application, we adopted the technology of multi-sensor fusion and transformed the acquired data from radar, navigation and image to log-polar coordinates, which makes us greatly simplify information when dealing with massive data problems from different sensors. This approach can not only reduce the complexity of the server’s processing but also drives the development of intelligent vehicle in information computing. We also make this model applied in the intelligent driver’s cognitive interactive debugging program, which can better explain and understand the intelligent driving behavior and improved the safety of intelligent vehicle to some extent.     

Multi-ion versus single-ion conduction mechanisms can yield current rectification in biological ion channels

There is clear evidence that the net magnitude of negative charge at the intracellular end of inwardly rectifying potassium channels helps to generate an asymmetry in the magnitude of the current that will pass in each direction. However, a complete understanding of the physical mechanism that links these charges to current rectification has yet to be obtained. Using Brownian dynamics, we compare the conduction mechanism and binding sites in rectifying and non-rectifying channel models. We find that in our models, rectification is a consequence of asymmetry in the hydrophobicity and charge of the pore lining. As a consequence, inward conduction can occur by a multi-ion conduction mechanism. However, outward conduction is restricted, since there are fewer ions at the intracellular entrance and outwardly moving ions must cross the pore on their own. We pose the question as to whether the same mechanism could be at play in inwardly rectifying potassium channels.     

Regional Frontal Gray Matter Volume Associated with Executive Function Capacity as a Risk Factor for Vehicle Crashes in Normal Aging Adults

Although low executive functioning is a risk factor for vehicle crashes among elderly drivers, the neural basis of individual differences in this cognitive ability remains largely unknown. Here we aimed to examine regional frontal gray matter volume associated with executive functioning in normal aging individuals, using voxel-based morphometry (VBM). To this end, 39 community-dwelling elderly volunteers who drove a car on a daily basis participated in structural magnetic resonance imaging, and completed two questionnaires concerning executive functioning and risky driving tendencies in daily living. Consequently, we found that participants with low executive function capacity were prone to risky driving. Furthermore, VBM analysis revealed that lower executive function capacity was associated with smaller gray matter volume in the supplementary motor area (SMA). Thus, the current data suggest that SMA volume is a reliable predictor of individual differences in executive function capacity as a risk factor for vehicle crashes among elderly persons. The implication of our results is that regional frontal gray matter volume might underlie the variation in driving tendencies among elderly drivers. Therefore, detailed driving behavior assessments might be able to detect early neurodegenerative changes in the frontal lobe in normal aging adults.     

Analytical aspects of the Brownian motor effect in randomly flashing ratchets

The muscle contraction, operation of ATP synthase, maintaining the shape of a cell are believed to be secured by motor proteins, which can be modelled using the Brownian ratchet mechanism. We consider the randomly flashing ratchet model of a Brownian motor, where the particles can be in two states, only one of which is sensitive the applied spatially periodic potential (the mathematical setting is a pair of weakly coupled reaction-diffusion and Fokker–Planck equations). We prove that this mechanism indeed generates unidirectional transport by showing that the amount of mass in the wells of the potential decreases/increases from left to right. The direction of transport is unambiguously determined by the location of each minimum of the potential with respect to the so-called diffusive mean of its adjacent maxima. The transport can be generated not only by an asymmetric potential, but also by a symmetric potential and asymmetric transition rates, and as a consequence of the general result we derive explicit conditions when the latter happens. When the transitions are localized on narrow active sites in the protein conformation space, we find a more explicit characterization of the bulk transport direction, and infer that some common preconditions of the motor effect are redundant.     

Action-based mechanisms of attention.

Actions, which have effects in the external world, must be spatiotopically represented in the brain. The brain is capable of representing space in many different forms (e.g. retinotopic-, environment-, head- or shoulder-centred), but we maintain that actions are represented in action-centred space, meaning that, at the cellular level, the direction of movement is defined by the activity of cells. In reaching, for example, object location is defined as the direction and distance between the origin of the hand and the target. Most importantly, we argue that more than one task-relevant action can be evoked at any moment in time. Therefore, highly efficient selection processes that accurately link vision and action have had to evolve. Research is reviewed which supports the notion of action-based inhibitory mechanisms that select the target from competing distractors.     

An analytical model for random pseudonym change scheme in VANETs

The emerging Vehicular Ad hoc NETworks (VANETs) can improve safety, efficiency and convenience in transportation. In spite of the expected benefits, VANETs also introduce some location privacy risk by periodically broadcasting vehicular information. Pseudonym change is commonly accepted as one approach to protect location privacy in VANETs, but the effectiveness of most pseudonym change schemes has never been investigated theoretically. In this paper, focusing on the Random Pseudonym Change (RPC) scheme, we propose an analytical model to quantify its level of location privacy by calculating the size of anonymity set of a vehicle. With this model, we can analyze the probability of a vehicle changing pseudonym simultaneously with its neighbor, which can be then used to calculate the size of anonymity set of the vehicle. Further, we investigate the influence of the pseudonym lifetime distribution on the level of location privacy. Research results, which are validated via extensive simulations, show that for the RPC scheme, the uniform pseudonym lifetime distribution can provide better location privacy protection than the reciprocal pseudonym lifetime distribution. In addition, we also discuss the conditional probability of a vehicle changing pseudonym simultaneously with its neighbor given the age of pseudonym used by the neighbor at the time when the vehicle last changed pseudonym, and point out that utilizing the age of pseudonym can improve the level of location privacy.     

Restoration of Motion-Blurred Image Based on Border Deformation Detection: A Traffic Sign Restoration Model

Due to the rapid development of motor vehicle Driver Assistance Systems (DAS), the safety problems associated with automatic driving have become a hot issue in Intelligent Transportation. The traffic sign is one of the most important tools used to reinforce traffic rules. However, traffic sign image degradation based on computer vision is unavoidable during the vehicle movement process. In order to quickly and accurately recognize traffic signs in motion-blurred images in DAS, a new image restoration algorithm based on border deformation detection in the spatial domain is proposed in this paper. The border of a traffic sign is extracted using color information, and then the width of the border is measured in all directions. According to the width measured and the corresponding direction, both the motion direction and scale of the image can be confirmed, and this information can be used to restore the motion-blurred image. Finally, a gray mean grads (GMG) ratio is presented to evaluate the image restoration quality. Compared to the traditional restoration approach which is based on the blind deconvolution method and Lucy-Richardson method, our method can greatly restore motion blurred images and improve the correct recognition rate. Our experiments show that the proposed method is able to restore traffic sign information accurately and efficiently.     

Efficient scheduling of team truck drivers in the European Union

This paper studies the problem of scheduling working hours of team drivers in European road freight transport where a sequence of ?? locations must be visited within given time windows. Since April 2007 working hours of truck drivers in the European Union must comply with regulation (EC) No 561/2006. These regulations impose standard limits on the daily driving times of truck drivers and extended daily limits that may only be used twice a week for each driver. We present a depth-first-breadth-second search method which can find a feasible schedule complying with standard daily driving time limits in O(??²) time, if such a schedule exists. Furthermore, we show that this method can also be used to find schedules complying with regulation (EC) No 561/2006 if daily driving times may exceed the standard limit.     

Mechanosensitive body–brain interactions in Caenorhabditis elegans

Proprioception and visceral mechanosensation provide important information about the location and deformation of the body parts in space and time. These deformations arise from muscle contraction during locomotion, but also from volume changes in organs that are subjected to stresses as a part of their physiological function. These internal morphodynamics give rise to periodic contraction–relaxation cycles with surprisingly constant amplitudes and the maintenance of these optimal driving patterns is remarkably robust against external and internal perturbations. One of the underlying reason for this robustness is an internal feedback mechanism in which specialized sensory cells and neurons signal the mechanical deformation of the inner workings of our organs, from the body to the brain, which subsequently adjust the driver to a predetermined physiological setpoint. Here, we review recent progress in the field of visceral mechanosensation and proprioception in Caenorhabditis elegans and discuss how future studies with this model can be used to gain insight into mechanosensory body–brain interactions in mammals.     

Contention-Aware Communication Schedule for High-Speed Communication

A lot of efforts have been devoted to address the software overhead problem in the past decade, which is known as the major hindrance on high-speed communication. However, this paper shows that having a low-latency communication system does not guarantee to achieve high performance, as there are other communication issues that have not been fully addressed by the use of low-latency communication, such as contention and scheduling of communication events. In this paper, we use the complete exchange operation as a case study to show that with careful design of communication schedules, we can achieve efficient communication as well as prevent congestion. We have developed a complete exchange algorithm, the Synchronous Shuffle Exchange, which is an optimal algorithm on the non-blocking network. To avoid congestion loss caused by the non-deterministic delays in communication events, a global congestion control scheme is introduced. This scheme coordinates all participating nodes to monitor and regulate the traffic load, which effectively avoids congestion loss and maintains sufficient throughput to maximize the performance. To improve the effectiveness of the congestion control scheme when working on the hierarchical network, we incorporate information on the network topology to devise a contention-aware permutation. This permutation scheme generates a communication schedule, which is both node and switch contention-free as well as distributing the network loads more evenly across the hierarchy. This relieves the congestion build-up at the uplink ports and improves the synchronism of the traffic information exchange between cluster nodes. Performance results of our implementation on a 32-node cluster with various network configurations are examined and reported in this paper.     

Force generation by cellular motors

Cell motility processes in non-muscle cells depend on the activity of motor proteins that bind to either microtubules or actin filaments. From presently available data it must be concluded that the driving force is generated by transient interaction of the respective motors with microtubules or actin filaments which then activates the binding and hydrolysis of ATP. This reaction results in an abrupt discharge of the motor molecule, the direction of which is determined by the spatial orientation of its binding to the helical and polar vehicle. The latter is thereby propelled in its length direction and simultaneously undergoes an axial rotation, while the expelled motor exerts an oppositely directed current in the surrounding fluid, comparable to jet propulsion. Force production, propulsion velocities and energy requirements known from in vitro studies comply with those derived from the theory. The theory opens new ways for the understanding of cellular activities such as particle transport, mitosis and morphodynamics.     

Who's afraid of a cognitive neuroscience of creativity?

This article has two goals. First, the ideas outlined here can be seen as a sustained and disciplined demolition project aimed at sanitizing our bad habits of thinking about creativity. Apart from the enormous amount of fluff out there, the study of creativity is, quite unfortunately, still dominated by a number of rather dated ideas that are either so simplistic that nothing good can possibly come out of them or, given what we know about the brain, factually mistaken. As cognitive neuroscience is making more serious contact with the knowledge base of creativity, we must, from the outset, clear the ground of these pernicious fossil traces from a bygone era. The best neuroimaging techniques help little if we don't know what to look for. Second, as an antidote to these theoretical duds, the article offers fresh ideas on possible mechanisms of creativity. Given that they are grounded in current understanding of cognitive and neural processes, it is hoped that these ideas represent steps broadly pointing in the right direction. In the end, the fundamental question we must ask ourselves is what, exactly, are the mental processes--or their critical elements--that yield creative thoughts.     

Complex networks and deep learning for EEG signal analysis

Electroencephalogram (EEG) signals acquired from brain can provide an effective representation of the human’s physiological and pathological states. Up to now, much work has been conducted to study and analyze the EEG signals, aiming at spying the current states or the evolution characteristics of the complex brain system. Considering the complex interactions between different structural and functional brain regions, brain network has received a lot of attention and has made great progress in brain mechanism research. In addition, characterized by autonomous, multi-layer and diversified feature extraction, deep learning has provided an effective and feasible solution for solving complex classification problems in many fields, including brain state research. Both of them show strong ability in EEG signal analysis, but the combination of these two theories to solve the difficult classification problems based on EEG signals is still in its infancy. We here review the application of these two theories in EEG signal research, mainly involving brain–computer interface, neurological disorders and cognitive analysis. Furthermore, we also develop a framework combining recurrence plots and convolutional neural network to achieve fatigue driving recognition. The results demonstrate that complex networks and deep learning can effectively implement functional complementarity for better feature extraction and classification, especially in EEG signal analysis.     

Space availability influence laterality in donkeys (Equus asinus)

Cerebral lateralization is the portioning of the cognitive functions between the two cerebral hemispheres. Several factors, like embryological manipulations, light exposure, health conditions, sex and age can influence the left-right brain asymmetries and contribute to increasing the variability in the strength and direction of laterality within most species. We investigated the influence of an environmental constraint, namely space availability, as a new source of variation on laterality in an adult vertebrate model, the donkey. In a baseline condition we tested whether donkeys show a motor lateralization bias at population level, while in an experimental condition we manipulated space availability to verify if a reduction in this parameter could represent a new source of variation in laterality. Results show that donkeys are lateralized at population level with a strong bias to standing with the right forelimb advanced over the left and that a reduction of space availability is an important source of variation in the laterality strength and direction within this species. The comparative analysis of the environmental and developmental factors that give origin to neural and behavioural laterality in animal models will be very important for a better understanding of the evolutionary origin of such multifaceted phenomenon.     

Hope for the Best or Prepare for the Worst? Towards a Spatial Cognitive Bias Test for Mice

Cognitive bias, the altered information processing resulting from the background emotional state of an individual, has been suggested as a promising new indicator of animal emotion. Comparable to anxious or depressed humans, animals in a putatively negative emotional state are more likely to judge an ambiguous stimulus as if it predicts a negative event, than those in positive states. The present study aimed to establish a cognitive bias test for mice based on a spatial judgment task and to apply it in a pilot study to serotonin transporter (5-HTT) knockout mice, a well-established mouse model for the study of anxiety- and depression-related behavior. In a first step, we validated that our setup can assess different expectations about the outcome of an ambiguous stimulus: mice having learned to expect something positive within a maze differed significantly in their behavior towards an unfamiliar location than animals having learned to expect something negative. In a second step, the use of spatial location as a discriminatory stimulus was confirmed by showing that mice interpret an ambiguous stimulus depending on its spatial location, with a position exactly midway between a positive and a negative reference point provoking the highest level of ambiguity. Finally, the anxiety- and depression-like phenotype of the 5-HTT knockout mouse model manifested - comparable to human conditions - in a trend for a negatively distorted interpretation of ambiguous information, albeit this effect was not statistically significant. The results suggest that the present cognitive bias test provides a useful basis to study the emotional state in mice, which may not only increase the translational value of animal models in the study of human affective disorders, but which is also a central objective of animal welfare research.     

Improving the interpretability of climate landscape metrics: An ecological risk analysis of Japan's Marine Protected Areas

Conservation efforts strive to protect significant swaths of terrestrial, freshwater and marine ecosystems from a range of threats. As climate change becomes an increasing concern, these efforts must take into account how resilient‐protected spaces will be in the face of future drivers of change such as warming temperatures. Climate landscape metrics, which signal the spatial magnitude and direction of climate change, support a convenient initial assessment of potential threats to and opportunities within ecosystems to inform conservation and policy efforts where biological data are not available. However, inference of risk from purely physical climatic changes is difficult unless set in a meaningful ecological context. Here, we aim to establish this context using historical climatic variability, as a proxy for local adaptation by resident biota, to identify areas where current local climate conditions will remain extant and future regional climate analogues will emerge. This information is then related to the processes governing species’ climate‐driven range edge dynamics, differentiating changes in local climate conditions as promoters of species range contractions from those in neighbouring locations facilitating range expansions. We applied this approach to assess the future climatic stability and connectivity of Japanese waters and its network of marine protected areas (MPAs). We find 88% of Japanese waters transitioning to climates outside their historical variability bounds by 2035, resulting in large reductions in the amount of available climatic space potentially promoting widespread range contractions and expansions. Areas of high connectivity, where shifting climates converge, are present along sections of the coast facilitated by the strong latitudinal gradient of the Japanese archipelago and its ocean current system. While these areas overlap significantly with areas currently under significant anthropogenic pressures, they also include much of the MPA network that may provide stepping‐stone protection for species that must shift their distribution because of climate change.     

A model study of protein nascent chain and cotranslational folding using hydrophobic-polar residues

To study protein nascent chain folding during biosynthesis, we investigate the folding behavior of models of hydrophobic and polar (HP) chains at growing length using both two-dimensional square lattice model and an optimized three-dimensional 4-state discrete off-lattice model. After enumerating all possible sequences and conformations of HP heteropolymers up to length N = 18 and N = 15 in two and three-dimensional space, respectively, we examine changes in adopted structure, stability, and tolerance to single point mutation as the nascent chain grows. In both models, we find that stable model proteins have fewer folded nascent chains during growth, and often will only fold after reaching full length. For the few occasions where partial chains of stable proteins fold, these partial conformations on average are very similar to the corresponding parts of the final conformations at full length. Conversely, we find that sequences with fewer stable nascent chains and sequences with native-like folded nascent chains are more stable. In addition, these stable sequences in general can have many more point mutations and still fold into the same conformation as the wild type sequence. Our results suggest that stable proteins are less likely to be trapped in metastable conformations during biosynthesis, and are more resistant to point-mutations. Our results also imply that less stable proteins will require the assistance of chaperone and other factors during nascent chain folding. Taken together with other reported studies, it seems that cotranslational folding may not be a general mechanism of in vivo protein folding for small proteins, and in vitro folding studies are still relevant for understanding how proteins fold biologically.     

A survey of dynamical genetics.

The classical view of genetics is based on the central dogma of molecular biology that assigns to DNA a fundamental but static role. According to the dogma, DNA can be duplicated only in identical copies (except for random errors), and no smart mechanism can alter the information content of DNA: in more detail, the direction of transfer of the genetic information is only from DNA through RNA to proteins and never backwards. However, starting from the so-called dynamic genome (McClintock's jumping genes), and the so-called dynamic mutations (such as the trinucleotide expansion or, more generally, the instability of the number of tandem repeats of longer sequences), there is now a growing body of important cases where it is known that the DNA is altered in a more or less sophisticated way, often by smart enzymatic mechanisms. The study of all such dynamic phenomena and of their interpretations can be naturally called dynamical genetics. In this survey we examine a number of such dynamic phenomena, and also some phenomena of great biological importance that have no universally accepted explanation within a static approach to genetics, and for which a dynamical interpretation has been only proposed. Important examples are some controversial but interesting phenomena such as horizontal transmission and Creutzfeldt-Jakob Disease, and those peculiar DNA structures known as G-quadruplexes.