Q-CSMA: Queue-Length Based CSMA/CA Algorithms for Achieving Maximum Throughput and Low Delay in Wireless Networks

Recently, it has been shown that CSMA-type random access algorithms can achieve the maximum possible throughput in ad hoc wireless networks. However, these algorithms assume an idealized continuous-time CSMA protocol where collisions can never occur. In addition, simulation results indicate that the delay performance of these algorithms can be quite bad. On the other hand, although some simple heuristics (such as distributed approximations of greedy maximal scheduling) can yield much better delay performance for a large set of arrival rates, they may only achieve a fraction of the capacity region in general. In this paper, we propose a discrete-time version of the CSMA algorithm. Central to our results is a discrete-time distributed randomized algorithm which is based on a generalization of the so-called Glauber dynamics from statistical physics, where multiple links are allowed to update their states in a single time slot. The algorithm generates collision-free transmission schedules while explicitly taking collisions into account during the control phase of the protocol, thus relaxing the perfect CSMA assumption. More importantly, the algorithm allows us to incorporate mechanisms which lead to very good delay performance while retaining the throughput-optimality property. It also resolves the hidden and exposed terminal problems associated with wireless networks.Comment: 12 page

Evaluation of the preparedness of the children’s emergency rooms (CHER) in Southern Nigeria for service delivery

Background: The Children Emergency Room (CHER) is the first point of call for many sick children. A significant proportion of childhood and under five deaths in tertiary institutions takes place in the CHER. There is thus need for a high level skilled manpower and infrastructure in the CHER in readiness for service delivery.Objective: To assesses the preparedness of the children emergency room in tertiary institutions in southern Nigeria to successful management of children presenting to the emergency rooms.Methods: This study was a cross sectional, descriptive multicentre study carried out among nine Tertiary Hospitals in Southern Nigeria. Three tertiary hospitals were randomly selected from each of the three Geo political zones (South-South, South- East and South -West) in Southern Nigeria. A structured questionnaire was used to collect data about the Children Emergency Rooms in these hospitals. The obtained data was entered and analysed using SPSS version 21 and is presented as table.Results: All the centres have an emergency room. The number of doctors in CHER ranged from 7 to 22 while the number of nurses ranged from 10 to 24 persons with a nurse: bed ratio of 1:3-15. In all the centres, the CHER had a side laboratory, well stocked emergency drug shelf, pulse oximeters, oxygen cylinders, electrical and manual suction machines, ambu bags and nebulizers. However, none of the centres has functional manual defibrillator or an Automated External Defibrillator (AED). In 5 (55.6%) of the studied centres, the doctors and nurses have training on emergency triage. Also 5 (55.6%) centres have doctors with certification in emergency care, but none of the nurses in all the centres have any certification in emergency care. Three (33.3%) centres had staff trained with skills on the use of AED while in 4(44.4%) centres they were skilled on the use of manual defibrillators. All the centres have a waiting area for patients’ relatives but only one (11.1%) has a television installed. All the CHERs have toilet facilities for patients relatives but only 5 (55.6%) have bathrooms. Running water is regularly available in the toilets of only 4 (44.4%) of the centres.Conclusion/Recommendation: We conclude that limitation abounds with regards to personnel, high technology infrastructure, personnel skill and patient friendly infrastructure. It is recommended that concerted efforts should be made by the government and all key players to make available the necessary equipment and facilities and ensure that health personnel acquire the necessary skills so that the standard of practice in our tertiary hospitals will be comparable to international best practices

MechAgents: Large language model multi-agent collaborations can solve mechanics problems, generate new data, and integrate knowledge

Solving mechanics problems using numerical methods requires comprehensive intelligent capability of retrieving relevant knowledge and theory, constructing and executing codes, analyzing the results, a task that has thus far mainly been reserved for humans. While emerging AI methods can provide effective approaches to solve end-to-end problems, for instance via the use of deep surrogate models or various data analytics strategies, they often lack physical intuition since knowledge is baked into the parametric complement through training, offering less flexibility when it comes to incorporating mathematical or physical insights. By leveraging diverse capabilities of multiple dynamically interacting large language models (LLMs), we can overcome the limitations of conventional approaches and develop a new class of physics-inspired generative machine learning platform, here referred to as MechAgents. A set of AI agents can solve mechanics tasks, here demonstrated for elasticity problems, via autonomous collaborations. A two-agent team can effectively write, execute and self-correct code, in order to apply finite element methods to solve classical elasticity problems in various flavors (different boundary conditions, domain geometries, meshes, small/finite deformation and linear/hyper-elastic constitutive laws, and others). For more complex tasks, we construct a larger group of agents with enhanced division of labor among planning, formulating, coding, executing and criticizing the process and results. The agents mutually correct each other to improve the overall team-work performance in understanding, formulating and validating the solution. Our framework shows the potential of synergizing the intelligence of language models, the reliability of physics-based modeling, and the dynamic collaborations among diverse agents, opening novel avenues for automation of solving engineering problems

Adamantane-1-ammonium benzoate

In the title molecular salt, C10H15NH3 +·C7H5O2 −, both carboxyl O atoms act as acceptors for strong N—H⋯O inter­molecular hydrogen-bond inter­actions with the ammonium group in the cation, generating infinite chains along the b axis. A weak C—H⋯π inter­action is also present

SE-KGE: A Location-Aware Knowledge Graph Embedding Model for Geographic Question Answering and Spatial Semantic Lifting

Learning knowledge graph (KG) embeddings is an emerging technique for a variety of downstream tasks such as summarization, link prediction, information retrieval, and question answering. However, most existing KG embedding models neglect space and, therefore, do not perform well when applied to (geo)spatial data and tasks. For those models that consider space, most of them primarily rely on some notions of distance. These models suffer from higher computational complexity during training while still losing information beyond the relative distance between entities. In this work, we propose a location-aware KG embedding model called SE-KGE. It directly encodes spatial information such as point coordinates or bounding boxes of geographic entities into the KG embedding space. The resulting model is capable of handling different types of spatial reasoning. We also construct a geographic knowledge graph as well as a set of geographic query-answer pairs called DBGeo to evaluate the performance of SE-KGE in comparison to multiple baselines. Evaluation results show that SE-KGE outperforms these baselines on the DBGeo dataset for geographic logic query answering task. This demonstrates the effectiveness of our spatially-explicit model and the importance of considering the scale of different geographic entities. Finally, we introduce a novel downstream task called spatial semantic lifting which links an arbitrary location in the study area to entities in the KG via some relations. Evaluation on DBGeo shows that our model outperforms the baseline by a substantial margin.Comment: Accepted to Transactions in GI

Water, rather than temperature, dominantly impacts how soil fauna affect dissolved carbon and nitrogen release from fresh litter during early litter decomposition

Longstanding observations suggest that dissolved materials are lost from fresh litter through leaching, but the role of soil fauna in controlling this process has been poorly documented. In this study, a litterbag experiment employing litterbags with different mesh sizes (3 mm to permit soil fauna access and 0.04 mm to exclude fauna access) was conducted in three habitats (arid valley, ecotone and subalpine forest) with changes in climate and vegetation types to evaluate the effects of soil fauna on the concentrations of dissolved organic carbon (DOC) and total dissolved nitrogen (TDN) during the first year of decomposition. The results showed that the individual density and community abundance of soil fauna greatly varied among these habitats, but Prostigmata, Isotomidae and Oribatida were the dominant soil invertebrates. At the end of the experiment, the mass remaining of foliar litter ranged from 58% for shrub litter to 77% for birch litter, and the DOC and TDN concentrations decreased to 54%-85% and increased to 34%-269%, respectively, when soil fauna were not present. The effects of soil fauna on the concentrations of both DOC and TDN in foliar litter were greater in the subalpine forest (wetter but colder) during the winter and in the arid valley (warmer but drier) during the growing season, and this effect was positively correlated with water content. Moreover, the effects of fauna on DOC and TDN concentrations were greater for high-quality litter and were related to the C/N ratio. These results suggest that water, rather than temperature, dominates how fauna affect the release of dissolved substances from fresh litter

ForceGen: End-to-end de novo protein generation based on nonlinear mechanical unfolding responses using a protein language diffusion model

Through evolution, nature has presented a set of remarkable protein materials, including elastins, silks, keratins and collagens with superior mechanical performances that play crucial roles in mechanobiology. However, going beyond natural designs to discover proteins that meet specified mechanical properties remains challenging. Here we report a generative model that predicts protein designs to meet complex nonlinear mechanical property-design objectives. Our model leverages deep knowledge on protein sequences from a pre-trained protein language model and maps mechanical unfolding responses to create novel proteins. Via full-atom molecular simulations for direct validation, we demonstrate that the designed proteins are novel, and fulfill the targeted mechanical properties, including unfolding energy and mechanical strength, as well as the detailed unfolding force-separation curves. Our model offers rapid pathways to explore the enormous mechanobiological protein sequence space unconstrained by biological synthesis, using mechanical features as target to enable the discovery of protein materials with superior mechanical properties