Abstract
We show that black hole high-mass X-ray binaries (HMXBs) with O- or B-type donor stars and relatively short orbital periods, of order one week to several months may survive spiral-in, to ...then form Wolf–Rayet (WR) X-ray binaries with orbital periods of order a day to a few days; while in systems where the compact star is a neutron star, HMXBs with these orbital periods never survive spiral-in. We therefore predict that WR X-ray binaries can only harbour black holes. The reason why black hole HMXBs with these orbital periods may survive spiral-in is: the combination of a radiative envelope of the donor star and a high mass of the compact star. In this case, when the donor begins to overflow its Roche lobe, the systems are able to spiral in slowly with stable Roche lobe overflow, as is shown by the system SS433. In this case, the transferred mass is ejected from the vicinity of the compact star (so-called isotropic re-emission mass-loss mode, or SS433-like mass-loss), leading to gradual spiral-in. If the mass ratio of donor and black hole is ≳3.5, these systems will go into common-envelope evolution and are less likely to survive. If they survive, they produce WR X-ray binaries with orbital periods of a few hours to one day. Several of the well-known WR+O binaries in our Galaxy and the Magellanic Clouds, with orbital periods in the range between a week and several months, are expected to evolve into close WR–black hole binaries, which may later produce close double black holes. The galactic formation rate of double black holes resulting from such systems is still uncertain, as it depends on several poorly known factors in this evolutionary picture. It might possibly be as high as ∼10−5 yr−1.
We present mass-loss predictions from Monte Carlo radiative transfer models for helium (He) stars as a function of stellar mass, down to 2 M⊙. Our study includes both massive Wolf-Rayet (WR) stars ...and low-mass He stars that have lost their envelope through interaction with a companion. For these low-mass He stars we predict mass-loss rates that are an order of magnitude smaller than by extrapolation of empirical WR mass-loss rates. Our lower mass-loss rates make it harder for these elusive stripped stars to be discovered via line emission, and we should attempt to find these stars through alternative methods instead. Moreover, lower mass-loss rates make it less likely that low-mass He stars provide stripped-envelope supernovae (SNe) of type Ibc. We express our mass-loss predictions as a function of L and Z and not as a function of the He abundance, as we do not consider this physically astute given our earlier work. The exponent of the Ṁ versus Z dependence is found to be 0.61, which is less steep than relationships derived from recent empirical atmospheric modelling. Our shallower exponent will make it more challenging to produce “heavy” black holes of order 40 M⊙, as recently discovered in the gravitational wave event GW 150914, making low metallicity for these types of events even more necessary.
Recycled aggregates (RAGs) usage in concrete is surging, inspired by environmental and economic concerns. Regarding predicting various models designed the values of modulus of elasticity (MOE) of ...concrete with natural aggregates and, in conclusion, they would probably be unreliable when used to concrete with RAG. In the present study, two new gray wolf multi‐layer perceptron neural networks (GWMLP) and gray wolf support vector regression (GWSVR) algorithms were proposed to predict RAG concrete's elastic modulus. About 400 records were gathered from published articles to develop these models. The results show that among the GWMLP models with different hidden layers, GWM3L with three hidden layers could get the highest score (TRS) at 39. Simultaneously, in the testing phase, the GWSVR was the first‐rank model because of the lower RMSE (0.6381), MAE (0.1541), and a larger R2 (0.9707) compared with GWMLP models. Therefore, it can result that the GWSVR model could predict the elastic modulus of RAG concrete precisely even better than GWM3L, which is well over the accuracy of the developed models.
In the contiguous 48 United States, southern Canada, and in Europe, wolves (Canis lupus) have greatly increased and expanded their range during the past few decades.They are prolific, disperse long ...distances, readily recolonize new areas where humans allow them, and are difficult to control when populations become established.Because wolves originally lived nearly everywhere throughout North America and Eurasia, and food in the form of wild and domestic prey is abundant there, many conservation-minded people favor wolves inhabiting even more areas.On the other hand, wolves conflict in several ways with rural residents who prefer fewer wolves. This article discusses the recovery of wolves, their benefits and values, the ways in which they conflict with humans, and the potential for their expansion into new areas.It concludes that wolf conservation will best be accomplished by each responsible political entity adaptively prescribing different management strategies for different zones within its purview.Some zones for some periods can support total protection, whereas in others, wolf numbers will have to be reduced to various degrees or removed.
•Wolves are recolonizing many areas in the United States, southern and eastern Canada, and Europe.•Once established, wolves are prolific and wolf populations are difficult to control.•Many urbanites revere wolves whereas many rural residents fear and dislike them, resulting in considerable controversy.•Wolves could live in many more places if livestock were better protected and more wild land could be preserved.•Ultimately, the use of adaptive management and zoning will be required to most sustainably foster wolf conservation
In recent years, confronted with serious global warming and rapid exhaustion of non-renewable resources, green manufacturing has become an increasingly important theme in the world. As a significant ...way to achieve the purpose of green manufacturing, the energy-efficient scheduling has been intensively studied by both academia and industry due to its ability to keep a compromise between production efficiency and environmental impacts. To this end, this study investigates the multi-objective flexible job shop scheduling problem (MOFJSP) with variable processing speeds aiming at minimizing the makespan and total energy consumption simultaneously. An elaborately-designed multi-objective grey wolf optimization (MOGWO) algorithm is proposed to address this issue. Specifically, a three-vector representation corresponding to three sub-problems including machine assignment, speed assignment and operation sequence is utilized for chromosome encoding. A new decoding method (NDM) is presented to obtain active schedules and reach a trade-off between two conflicting criteria. In consideration of the multi-objective problem nature, two Pareto-based mechanisms are developed to determine the leader wolves and the lowest (worst) wolves so that the hierarchy of a wolf pack can be constructed. Finally, to avoid premature convergence and maintain population diversity, a new position updating mechanism (NPUM), which integrates information from both the leader wolves and the lowest wolves based on a comprehensive point of view, is developed to guide the other wolves in the searching space. Extensive numerical experiments on 35 different scale benchmarks have not only verified the effectiveness of NDM and NPUM but also demonstrated that the proposed MOGWO is more effective than well-known multi-objective evolutionary algorithms such as NSGA-II and SPEA-II.
•A multi-objective grey wolf optimization (MOGWO) algorithm is proposed for the MOFJSP with variable processing speeds to minimize makespan and total energy consumption.•Two Pareto-based mechanisms are presented to determine the leader wolves and the lowest wolves.•A new decoding method (NDM) is developed to obtain active schedules as well as reach a trade-off between makespan and total energy consumption.•A new position updating mechanism (NPUM) integrating information from both the leader wolves and the lowest wolves is designed to guide the searching process.•Extensive numerical experiments on 35 benchmarks have confirmed the effectiveness of the MOGWO.
Wolf-Hirschhorn syndrome (WHS) is a contiguous gene disorder consisting of prenatal and postnatal growth deficiency, distinctive craniofacial features, intellectual disability, and seizures. The ...condition is caused by a partial loss of material from the distal portion of the short arm of chromosome 4 (4p16.3). While there are many reports of individuals with WHS, useful data on long-term survival and life status of adults with the syndrome are very limited. There are only 11 reports of individuals over the age of 18 years in the literature. Establishing the medical manifestations of adults with WHS would be helpful in establishing appropriate health supervision guidelines. This study was one component of a two-part investigation on adults with WHS. This patient-reported outcomes study (PROS) was accomplished by using the registry of rare diseases at Sanford Research, Coordination of Rare Diseases (CoRDS)at Sanford. Thirty family members or caretakers of 30 adults with WHS/4p- entered into the CoRDS registry and completed some or all of the survey data. Twelve caretakers completed the recently-added survey on activities of daily living. Two of the individuals with WHS were partly independent while 10 required total care. The results provide novel information on daily life and independence in adults with WHS. Importantly, the majority of caretakers reported that the adults were in good health. The data from both parts of the study will contribute to our knowledge of the natural history of the syndrome and guide in establishing appropriate health supervision guidelines for adults with WHS.
One of methods for loss reduction and reliability improvement of radial distribution system is using of renewable energy generation. In this paper, a new optimal placement and sizing of renewable ...energy sources based on photovoltaic panels (PVs) and wind turbines (WTs) in the distribution network is presented with the objective of loss reduction and reliability improvement based on energy not-supplied (ENS). A multi-objective evolutionary algorithm based on fuzzy decision-making method, called the Multi-Objective Hybrid Teaching–Learning Based Optimization-Grey Wolf Optimizer (MOHTLBOGWO) is proposed to solve the optimization problem. The proposed hybrid method has a high convergence speed and not trapped at all in local optimal. The proposed method is implemented in the form of single-objective and multi-objective on 33 and 69 bus IEEE radial distribution networks. The simulation results clear that the multi-objective optimization is a more precise approach to network utilization taking into account all objective indices than the single objective method. The results show that the proposed method has better convergence speed and less convergence tolerance in achieving to best solution in comparison with TLBO and GWO methods in loss reduction, reliability improvement and increasing the net saving and also in comparison with last studies. Moreover, the results show that dispersion of the size and location of distributed renewable generation leads to a further reduction in losses and a better improvement of the reliability criterion.
•MOTLBOGWO is proposed for optimal placement and sizing of PVs and WTs.•The objective function is designed to reduce loss and reliability improvement.•The placement results are compared in single and multi-objective optimization.•The effectiveness of proposed method is verified compared with other algorithms.•PVs and WTs dispersion is caused less loss and more reliability improvement.
Path planning for robots plays a vital role to seek the most feasible path due to power requirement, environmental factors and other limitations. The path planning for the autonomous robots is ...tedious task as the robot needs to locate a suitable path to move between the source and destination points with multifaceted nature. In this paper, we introduced a new technique named modified grey wolf optimization (MGWO) algorithm to solve the path planning problem for multi-robots. MGWO is modified version of conventional grey wolf optimization (GWO) that belongs to the category of metaheuristic algorithms. This has gained wide popularity for an optimization of different parameters in the discrete search space to solve various problems. The prime goal of the proposed methodology is to determine the optimal path while maintaining a sufficient distance from other objects and moving robots. In MGWO method, omega wolves are treated equally as those of delta wolves in exploration process that helps in escalating the convergence speed and minimizing the execution time. The simulation results show that MGWO gives satisfactory performance than other state of art methods for path planning of multiple mobile robots. The performance of the proposed method is compared with the standard evolutionary algorithms viz., Particle Swarm Optimization (PSO), Intelligent BAT Algorithm (IBA), Grey Wolf Optimization (GWO), and Variable Weight Grey Wolf Optimization (VW-GWO) and yielded better results than all of these.
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