Scheduling reentrant jobs on parallel machines with a remote server

This paper explores a specific combinatorial problem relating to re-entrant jobs on parallel primary machines, with a remote server machine. A middle operation is required by each job on the server before it returns to its primary processing machine. The problem is inspired by the logistics of a semi-automated micro-biology laboratory. The testing programme in the laboratory corresponds roughly to a hybrid flowshop, whose bottleneck stage is the subject of study. We demonstrate the NP-hard nature of the problem, and provide various structural features. A heuristic is developed and tested on randomly generated benchmark data. Results indicate solutions reliably within 1.5% of optimum. We also provide a greedy 2-approximation algorithm. Test on real-life data from the microbiology laboratory indicate a 20% saving relative to current practice, which is more than can be achieved currently with 3 instead of 2 people staffing the primary machines

Cartesian control of redundant robots

A Cartesian-space position/force controller is presented for redundant robots. The proposed control structure partitions the control problem into a nonredundant position/force trajectory tracking problem and a redundant mapping problem between Cartesian control input F is a set member of the set R(sup m) and robot actuator torque T is a set member of the set R(sup n) (for redundant robots, m is less than n). The underdetermined nature of the F yields T map is exploited so that the robot redundancy is utilized to improve the dynamic response of the robot. This dynamically optimal F yields T map is implemented locally (in time) so that it is computationally efficient for on-line control; however, it is shown that the map possesses globally optimal characteristics. Additionally, it is demonstrated that the dynamically optimal F yields T map can be modified so that the robot redundancy is used to simultaneously improve the dynamic response and realize any specified kinematic performance objective (e.g., manipulability maximization or obstacle avoidance). Computer simulation results are given for a four degree of freedom planar redundant robot under Cartesian control, and demonstrate that position/force trajectory tracking and effective redundancy utilization can be achieved simultaneously with the proposed controller

Pulse rates recorded by digital film positioner

System converts detector pulse rates to photographs of binary scale indicator lights on continuously moving film. The system then scans the film and transfers the data to computer-compatible magnetic tape

Entrainment and chaos in a pulse-driven Hodgkin-Huxley oscillator

The Hodgkin-Huxley model describes action potential generation in certain types of neurons and is a standard model for conductance-based, excitable cells. Following the early work of Winfree and Best, this paper explores the response of a spontaneously spiking Hodgkin-Huxley neuron model to a periodic pulsatile drive. The response as a function of drive period and amplitude is systematically characterized. A wide range of qualitatively distinct responses are found, including entrainment to the input pulse train and persistent chaos. These observations are consistent with a theory of kicked oscillators developed by Qiudong Wang and Lai-Sang Young. In addition to general features predicted by Wang-Young theory, it is found that most combinations of drive period and amplitude lead to entrainment instead of chaos. This preference for entrainment over chaos is explained by the structure of the Hodgkin-Huxley phase resetting curve.Comment: Minor revisions; modified Fig. 3; added reference

A 2-chain can interlock with a k-chain

One of the open problems posed in [3] is: what is the minimal number k such that an open, flexible k-chain can interlock with a flexible 2-chain? In this paper, we establish the assumption behind this problem, that there is indeed some k that achieves interlocking. We prove that a flexible 2-chain can interlock with a flexible, open 16-chain.Comment: 10 pages, 6 figure

Continuous monitoring of the lunar or Martian subsurface using on-board pattern recognition and neural processing of Rover geophysical data

The ultimate goal is to create an extraterrestrial unmanned system for subsurface mapping and exploration. Neural networks are to be used to recognize anomalies in the profiles that correspond to potentially exploitable subsurface features. The ground penetrating radar (GPR) techniques are likewise identical. Hence, the preliminary research focus on GPR systems will be directly applicable to seismic systems once such systems can be designed for continuous operation. The original GPR profile may be very complex due to electrical behavior of the background, targets, and antennas, much as the seismic record is made complex by multiple reflections, ghosting, and ringing. Because the format of the GPR data is similar to the format of seismic data, seismic processing software may be applied to GPR data to help enhance the data. A neural network may then be trained to more accurately identify anomalies from the processed record than from the original record

The Effect of Religion on Trait Priority in Potential Partners in Short and Long Term Relationships

This study builds upon previous research that demonstrates how various demographic characteristics, as well as relationship type, affects trait preferences for potential mate partners. This study also explores the potential effects of religious affiliation and religiosity on trait preferences, as no previous research explicitly tests how individuals’ religious affiliation or strength of religiosity influences their rank ordering of potential partner traits. Seven hypotheses were proposed, as well as four points of exploration regarding sexuality, relationship status, religious affiliation, and race were proposed. Hypotheses 1, which predicted that women will rank financial stability higher than men and that men will rank physical attractiveness higher than women; Hypothesis 2, which predicted that gay people will rank religiously-oriented traits and the desire for the same number of children lower than their straight counterparts; Hypothesis 3, which predicted that gay men will rank financial stability higher than straight men and that lesbians will rank intelligence higher than straight women; and Hypothesis 4, which predicted that older people will rank religiously-oriented traits and political and moral similarity higher than younger people, who will rank humor higher, were supported. However, Hypothesis 5, which predicted that religious people will rank religiously-oriented traits higher than non-religious people, was not supported, with results suggesting an effect opposite to the effect predicted. Hypothesis 6, which predicted that more group differences will be present for long term relationships than short term relationships was mostly supported. Hypothesis 7, which predicted that results will remain consistent across nationalities, was unable to be tested because of inconsistencies in the data. Effects of the proposed exploratory demographics were identified. Possible explanations for unpredicted and exploratory results, as well as limitations and future directions, including remaining gaps in research, are discussed

Enhanced response of the regular networks to local signals in presence of a fast impurity

We consider an array of inductively coupled Josephson junctions with a fast impurity (a junction with a smaller value of critical current), and study the consequences of imposing a small amplitude periodic signal at some point in the array. We find that when external signal is imposed at the impurity, the response of the array is boosted and a small amplitude signal can be detected throughout the array. When the signal is imposed elsewhere, minor effects is seen on the dynamics of the array. The same results have been also seen in presence of a single fast spiking neuron in a chain of diffusively coupled FitzHugh-Nagumo neurons.Comment: 6 pages, 5 figures, arXiv admin note: substantial text overlap with arXiv:1108.460

An Improved Measure of Deaths due to COVID-19 in England and Wales

We address the question of: 'how many deaths in England and Wales are due to COVID-19?' There are two approaches to measuring COVID deaths - 'COVID associated deaths' and 'excess deaths'. An excess deaths type framework is preferable, as there is substantial measurement error in COVID associated deaths, due to issues relating to the identification of deaths that are directly attributable to COVID-19. A limitation of the current excess deaths metric (a comparison of deaths to a 5 year average for the same week), is that it attributes the entirety of the variation in mortality to COVID-19. This likely means that the metric is overstated because there are a range of other drivers of mortality. We address this by estimating novel empirical Poisson models for all-cause deaths (in totality; by age category; for males; and females) that account for other drivers including the lockdown Government policy response. The models are novel because they include COVID identifier variables (which are a variation on a dummy variable). We use these identifiers to estimate weekly deviations in COVID deaths (about the mean weekly estimate pertaining to the COVID dummy variable in our baseline model). Results from two sets of identifiers indicate that, over the periods when our weekly estimates of total COVID deaths and the current excess deaths measure differ (week ending 17th or 24th April 2020 - week ending 8th May 2020), the former is considerably below the latter - on average per week 4670 deaths (54%) lower, or 4727 deaths (63%) lower, respectively

Dynamic multilevel modelling of industrial energy demand in Europe

Previous studies of industry level energy demand have not accounted for the hierarchical nesting of industries within a system that also adequately allows for country specific determinants of energy demand. The principal contribution of this paper is therefore to analyse energy demand for European industries over the period 1995–2009 using a dynamic multilevel model that accounts for this hierarchical data structure. Among other things, we find, firstly, that our dynamic multilevel model suggests that if industry income and the industry energy price increase by 10%, long run energy demand will increase by 8.1% and fall by 6.8%, respectively. Secondly, we find that the corresponding long run income and price elasticities are substantially larger in a standard dynamic model of industry level energy demand which does not account for the hierarchical data structure. Our results therefore suggest that not accounting for the hierarchical data structure results in unreliable estimates of energy demand elasticities. From a policy perspective we argue that it is imperative that future industry level energy demand studies account for the hierarchical structure of the data. This is to prevent energy policy making being based on industry level evidence that substantially inflates the responsiveness of long run energy demand to income and price changes