Temperature Matrix-Based Data Placement Optimization in Edge Computing Environment

The scale of data shows an explosive growth trend, with wide use of cloud storage. However, there are challenges such as network latency and energy consumption. The emergence of edge computing brings data close to the edge of the network, making it a good supplement to cloud computing. The spatiotemporal characteristics of data have been largely ignored in studies of data placement and storage optimization. To this end, a temperature matrix-based data placement method using an improved Hungarian algorithm (TEMPLIH) is proposed in this work. A temperature matrix is used to reflect the influence of data characteristics on its placement. A data replica matrix selection algorithm based on temperature matrix (RSA-TM) is proposed to meet latency requirements. Then, an improved Hungarian algorithm based on replica matrix (IHA-RM) is proposed, which satisfies the balance among the multiple goals of latency, cost, and load balancing. Compared with other data placement strategies, experiments show that the proposed method can effectively reduce the cost of data placement while meeting user access latency requirements and maintaining a reasonable load balance between edge servers. Further improvement is discussed and the idea of regional value is proposed

Decelerating Airy pulse propagation in highly non-instantaneous cubic media

The propagation of decelerating Airy pulses in non-instantaneous cubic medium is investigated both theoretically and numerically. In a Debye model, at variance with the case of accelerating Airy and Gaussian pulses, a decelerating Airy pulse evolves into a single soliton for weak and general non- instantaneous response. Airy pulses can hence be used to control soliton generation by temporal shaping. The effect is critically dependent on the response time, and could be used as a way to measure the Debye type response function. For highly non- instantaneous response, we theoretically find a decelerating Airy pulse is still transformed into Airy wave packet with deceleration. The theoretical predictions are confirmed by numerical simulations

Optimal Planning for Deepwater Oilfield Development Under Uncertainties of Crude Oil Price and Reservoir

The development planning of deepwater oilfield directly influences production costs and benefits. However, the uncertainties of crude oil price and reservoir and the special production requirements make it difficult to optimize development planning of deepwater oilfield. Although there have been a number of scholars researching on this issue, previous models just focused on several special working conditions and few have considered energy supply of floating production storage and offloading (FPSO). In light of the normal deepwater production development cycles, in this paper, a multiscenario mixed integer linear programming (MS-MILP) method is proposed based on reservoir numerical simulation, considering the uncertainties of reservoir and crude oil price and the constraint of energy consumption of FPSO, to obtain the globally optimal development planning of deepwater oilfield. Finally, a real example is taken as the study objective. Compared with previous researches, the method proposed in this paper is testified to be practical and reliable

ANPL: Compiling Natural Programs with Interactive Decomposition

The advents of Large Language Models (LLMs) have shown promise in augmenting programming using natural interactions. However, while LLMs are proficient in compiling common usage patterns into a programming language, e.g., Python, it remains a challenge how to edit and debug an LLM-generated program. We introduce ANPL, a programming system that allows users to decompose user-specific tasks. In an ANPL program, a user can directly manipulate sketch, which specifies the data flow of the generated program. The user annotates the modules, or hole with natural language descriptions offloading the expensive task of generating functionalities to the LLM. Given an ANPL program, the ANPL compiler generates a cohesive Python program that implements the functionalities in hole, while respecting the dataflows specified in sketch. We deploy ANPL on the Abstraction and Reasoning Corpus (ARC), a set of unique tasks that are challenging for state-of-the-art AI systems, showing it outperforms baseline programming systems that (a) without the ability to decompose tasks interactively and (b) without the guarantee that the modules can be correctly composed together. We obtain a dataset consisting of 300/400 ARC tasks that were successfully decomposed and grounded in Python, providing valuable insights into how humans decompose programmatic tasks. See the dataset at https://iprc-dip.github.io/DARC

Prediction of trabecular bone architectural features by deep learning models using simulated DXA images

Dual-energy X-ray absorptiometry (DXA) is widely used for clinical assessment of bone mineral density (BMD). Recent evidence shows that DXA images may also contain microstructural information of trabecular bones. However, no current image processing techniques could aptly extract the information. Inspired by the success of deep learning techniques in medical image analyses, we hypothesized in this study that DXA image-based deep learning models could predict the major microstructural features of trabecular bone with a reasonable accuracy. To test the hypothesis, 1249 trabecular cubes (6 mm × 6 mm × 6 mm) were digitally dissected out from the reconstruction of seven human cadaveric proximal femurs using microCT scans. From each cube, simulated DXA images in designated projections were generated, and the histomorphometric parameters (i.e., BV/TV, BS, Tb.Th, DA, Conn. D, and SMI) of the cube were determined using Image J. Convolutional neural network (CNN) models were trained using the simulated DXA images to predict the histomorphometric parameters of trabecular bone cubes. The results exhibited that the CNN models achieved high fidelity in predicting these histomorphometric parameters (from R = 0.80 to R = 0.985), showing that the DL models exhibited the capability of predicting the microstructural features using DXA images. This study also showed that the number and resolution of input simulated DXA images had considerable impacts on the prediction accuracy of the DL models. These findings support the hypothesis of this study and indicate a high potential of using DXA images in prediction of osteoporotic bone fracture risk

The genome and gene editing system of sea barleygrass provide a novel platform for cereal domestication and stress tolerance studies

The tribe Triticeae provides important staple cereal crops and contains elite wild species with wide genetic diversity and high tolerance to abiotic stresses. Sea barleygrass (Hordeum marinum Huds.), a wild Triticeae species, thrives in saline marshlands and is well known for its high tolerance to salinity and waterlogging. Here, a 3.82-Gb high-quality reference genome of sea barleygrass is assembled de novo, with 3.69 Gb (96.8%) of its sequences anchored onto seven chromosomes. In total, 41 045 high-confidence (HC) genes are annotated by homology, de novo prediction, and transcriptome analysis. Phylogenetics, non-synonymous/synonymous mutation ratios (Ka/Ks), and transcriptomic and functional analyses provide genetic evidence for the divergence in morphology and salt tolerance among sea barleygrass, barley, and wheat. The large variation in post-domestication genes (e.g. IPA1 and MOC1) may cause interspecies differences in plant morphology. The extremely high salt tolerance of sea barleygrass is mainly attributed to low Na+ uptake and root-to-shoot translocation, which are mainly controlled by SOS1, HKT, and NHX transporters. Agrobacterium-mediated transformation and CRISPR/Cas9-mediated gene editing systems were developed for sea barleygrass to promote its utilization for exploration and functional studies of hub genes and for the genetic improvement of cereal crops

The αTSR Domain of Plasmodium Circumsporozoite Protein Bound Heparan Sulfates and Elicited High Titers of Sporozoite Binding Antibody After Displayed by Nanoparticles

INTRODUCTION: Malaria is a devastating infectious illness caused by protozoan Plasmodium parasites. The circumsporozoite protein (CSP) on Plasmodium sporozoites binds heparan sulfate proteoglycan (HSPG) receptors for liver invasion, a critical step for prophylactic and therapeutic interventions. METHODS: In this study, we characterized the αTSR domain that covers region III and the thrombospondin type-I repeat (TSR) of the CSP using various biochemical, glycobiological, bioengineering, and immunological approaches. RESULTS: We found for the first time that the αTSR bound heparan sulfate (HS) glycans through support by a fused protein, indicating that the αTSR is a key functional domain and thus a vaccine target. When the αTSR was fused to the S domain of norovirus VP1, the fusion protein self-assembled into uniform S 60-αTSR nanoparticles. Three-dimensional structure reconstruction revealed that each nanoparticle consists of an S 60 nanoparticle core and 60 surface displayed αTSR antigens. The nanoparticle displayed αTSRs retained the binding function to HS glycans, indicating that they maintained authentic conformations. Both tagged and tag-free S 60-αTSR nanoparticles were produced via the Escherichia coli system at high yield by scalable approaches. They are highly immunogenic in mice, eliciting high titers of αTSR-specific antibody that bound specifically to the CSPs of Plasmodium falciparum sporozoites at high titer. DISCUSSION AND CONCLUSION: Our data demonstrated that the αTSR is an important functional domain of the CSP. The S 60-αTSR nanoparticle displaying multiple αTSR antigens is a promising vaccine candidate potentially against attachment and infection of Plasmodium parasites