Silver spoon effects of hatching order in an asynchronous hatching bird

The silver spoon hypothesis proposes that individuals which develop under favourable conditions will gain fitness benefits throughout their lifetime. Hatching order may create a considerable size hierarchy within a brood and lead to earlier-hatched nestlings having a competitive advantage over their siblings, which has been illustrated in some studies. However, there have been few explorations into the effect on subsequent generations. Here, using a 15-year-long study, we investigated the long-term fitness consequence of hatching order in the endangered crested ibis, Nipponia nippon, a species with complete hatching asynchrony. In this study, we found strong support for silver spoon effects acting on hatching order. Compared to later-hatched nestlings, first-hatched nestlings begin reproduction at an earlier age, have higher adult survival rates, possess a longer breeding life span and achieve higher lifetime reproductive success. Interestingly, we found carry-over effects of hatching order into the next generation. Nestlings which hatched earlier and became breeders in turn also produced nestlings with larger tarsus and better body condition. Additionally, we found a positive correlation among life-history traits in crested ibis. Individuals which started reproduction at a younger age were shown to possess a longer breeding life span. And the annual brood size increased with an individual’s breeding life span. This suggests that the earlier-hatched nestlings are of better quality and the ‘silver spoon’ effects of hatching order cover all life-history stages and next generation effects

Experimental observation of magnetic bobbers for a new concept of magnetic solid-state memory

The use of chiral skyrmions, which are nanoscale vortex-like spin textures, as movable data bit carriers forms the basis of a recently proposed concept for magnetic solid-state memory. In this concept, skyrmions are considered to be unique localized spin textures, which are used to encode data through the quantization of different distances between identical skyrmions on a guiding nanostripe. However, the conservation of distances between highly mobile and interacting skyrmions is difficult to implement in practice. Here, we report the direct observation of another type of theoretically-predicted localized magnetic state, which is referred to as a chiral bobber (ChB), using quantitative off-axis electron holography. We show that ChBs can coexist together with skyrmions. Our results suggest a novel approach for data encoding, whereby a stream of binary data representing a sequence of ones and zeros can be encoded via a sequence of skyrmions and bobbers. The need to maintain defined distances between data bit carriers is then not required. The proposed concept of data encoding promises to expedite the realization of a new generation of magnetic solid-state memory

ARTNet:AI-based Resource Allocation and Task Offloading in a Reconfigurable Internet of Vehicular Networks

The convergence of Software-Defined Networking (SDN) and Internet of Vehicular (IoV) integrated with Fog Computing (FC), known as Software Defined Vehicular based FC (SDV-F), has recently been established to take advantage of both paradigms and efficiently control the wireless networks. SDV-F tackles numerous problems, such as scalability, load-balancing, energy consumption, and security. It lags, however, in providing a promising approach to enable ultra-reliable and delay-sensitive applications with high vehicle mobility over SDV-F. We propose ARTNet, an AI-based Vehicle-to-Everything (V2X) framework for resource distribution and optimized communication using the SDV-F architecture. ARTNet offers ultra-reliable and low-latency communications, particularly in highly dynamic environments, which is still a challenge in IoV. ARTNet is composed of intelligent agents/controllers, to make decisions intelligently about (i) maximizing resource utilization at the fog layer, and (ii) minimizing the average end-to-end delay of time-critical IoV applications. Moreover, ARTNet is designed to assign a task to fog nodes based on their states. Our experimental results show that considering a dynamic IoV environment, ARTNet can efficiently distribute the fog layer tasks while minimizing the delay

Space-time evolution of ultra-relativistic heavy ion collisions and hadronic spectra

The space-time evolution of the hot and dense matter formed after the collisions of heavy nuclei at ultra-relativistic energies is investigated using (3+1) dimensional hydrodynamical models. The effects of the spectral shift of the hadronic properties are incorporated in the equation of state (EOS) of the evolving matter. In-medium shift of hadronic properties are considered for Quantum Hadrodynamics (QHD) and universal scaling scenarios. It is found that the EOS for the hadronic matter for universal scaling of hadronic masses (except pseudoscalar mesons) is similar to the recent lattice results. We observe that the space-time volume of the hadronic matter at the freeze-out is considerably different from the one when medium effects on the hadrons are ignored. The sensitivity of the results on the initial radial velocity profile is investigated. The transverse mass spectra of pions and protons of NA49 collaboration are analyzed.Comment: Total 17 pages with 18 figure

HDAC2 Negatively Regulates Memory Formation and Synaptic Plasticity

Chromatin modifications, especially histone-tail acetylation, have been implicated in memory formation. Increased histone-tail acetylation induced by inhibitors of histone deacetylases (HDACis) facilitates learning and memory in wild-type mice as well as in mouse models of neurodegeneration. Harnessing the therapeutic potential of HDACis requires knowledge of the specific HDAC family member(s) linked to cognitive enhancement. Here we show that neuron-specific overexpression of HDAC2, but not that of HDAC1, decreased dendritic spine density, synapse number, synaptic plasticity and memory formation. Conversely, Hdac2 deficiency resulted in increased synapse number and memory facilitation, similar to chronic treatment with HDACis in mice. Notably, reduced synapse number and learning impairment of HDAC2-overexpressing mice were ameliorated by chronic treatment with HDACis. Correspondingly, treatment with HDACis failed to further facilitate memory formation in Hdac2-deficient mice. Furthermore, analysis of promoter occupancy revealed an association of HDAC2 with the promoters of genes implicated in synaptic plasticity and memory formation. Taken together, our results suggest that HDAC2 functions in modulating synaptic plasticity and long-lasting changes of neural circuits, which in turn negatively regulates learning and memory. These observations encourage the development and testing of HDAC2-selective inhibitors for human diseases associated with memory impairment

Di-μ-nicotinato-κ2 N:O;κ2 O:N-bis­[aqua­(ethyl­enediamine-κ2 N,N′)(nicotinato-κN)cadmium(II)] dihydrate

The dinuclear mol­ecule of the title compound, [Cd2(C6H4NO2)4(C2H8N2)2(H2O)2]·2H2O, lies on an inversion centre and forms 12-membered (CdNC3O)2 metallacycles with the two Cd2+ ions bridged by two nicotinate ligands. Both Cd2+ ions display coordination polyhedra with a distorted octa­hedral geometry that includes two pyridine N atoms from bridging and terminal nicotinate anions, two amine N atoms from chelating ethylene­diamine ligands, carboxylate O atoms from bridging nicotinate anions and water O atoms. Inter­molecular O—H⋯O and N—H⋯O hydrogen bonds result in the formation of a three-dimensional network, and π–π stacking inter­actions are observed between symmetry-related pyridine rings of bridging as well as terminal nicotinate anions (the centroid–centroid distances are 3.59 and 3.69 Å, respectively, and the distances between parallel planes of the stacked pyridine rings are 3.53 and 3.43 Å, respectively). The two methylene groups of the ethylene­diamine ligand are disordered over two positions; the site occupancy factors are ca 0.8 and 0.2

Inventory control with seasonality of lead times

The practical challenges posed by the seasonality of lead times have largely been ignored within the inventory control literature. The length of the seasons, as well as the length of the lead times during a season, may demonstrate cyclical patterns over time. This study examines whether inventory control policies that anticipate seasonal lead-time patterns can reduce costs. We design a framework for characterizing different seasonal lead-time inventory problems. Subsequently, we examine the effect of deterministic and stochastic seasonal lead times within periodic review inventory control systems. We conduct a base case analysis of a deterministic system, enabling two established and alternating lead-time lengths that remain valid through known intervals. We identify essential building blocks for developing solutions to seasonal lead-time problems. Lastly, we perform numerical experiments to evaluate the cost benefits of implementing an inventory control policy that incorporates seasonal lead-time lengths. The findings of the study indicate the potential for cost improvements. By incorporating seasonality in length of seasons and length of lead times within the season into the control models, inventory controllers can make more informed decisions when ordering their raw materials. They need smaller buffers against lead-time variations due to the cyclical nature of seasonality. Reductions in costs in our experiments range on average between 18.9 and 26.4% (depending on safety time and the probability of the occurrence of stock out). Therefore, inventory control methods that incorporate seasonality instead of applying large safety stock or safety time buffers can lead to substantial cost reductions

PFARS: Enhancing Throughput and Lifetime of Heterogeneous WSNs through Power-aware Fusion, Aggregation and Routing Scheme

Heterogeneous wireless sensor networks (WSNs) consist of resource-starving nodes that face a challenging task of handling various issues such as data redundancy, data fusion, congestion control, and energy efficiency. In these networks, data fusion algorithms process the raw data generated by a sensor node in an energy-efficient manner to reduce redundancy, improve accuracy, and enhance the network lifetime. In literature , these issues are addressed individually and most of the proposed solutions are either application-specific or too complex that make their implementation unrealis-tic, specifically, in a resource-constrained environment. In this paper, we propose a novel node level data fusion algorithm for heterogeneous WSNs to detect noisy data and replace them with highly refined data. To minimize the amount of transmitted data, a hybrid data aggregation algorithm is proposed that performs in-network processing while preserving the reliability of gathered data. This combination of data fusion and data aggregation algorithms effectively handle the aforementioned issues by ensuring an efficient utilization of the available resources. Apart from fusion and aggregation, a biased traffic distribution algorithm is introduced that considerably increases the overall lifetime of heterogeneous WSNs. The proposed algorithm performs the tedious task of traffic distribution according to the network's statistics, i.e., the residual energy of neighboring nodes and their importance from a network's con-nectivity perspective. All our proposed algorithms were tested on a real-time dataset obtained through our deployed heterogeneous WSN in an orange orchard, and also on publicly available benchmark datasets. Experimental results verify that our proposed algorithms outperform the existing approaches in term of various performance metrics such as throughput, lifetime, data accuracy, computational time and delay