Sharing Economy in Local Energy Markets

With an increase in the electrification of end-use sectors, various resources on the demand side provide great flexibility potential for system operation, which also leads to problems such as the strong randomness of power consumption behavior, the low utilization rate of flexible resources, and difficulties in cost recovery. With the core idea of 'access over ownership', the concept of the sharing economy has gained substantial popularity in the local energy market in recent years. Thus, we provide an overview of the potential market design for the sharing economy in local energy markets (LEMs) and conduct a detailed review of research related to local energy sharing, enabling technologies, and potential practices. This paper can provide a useful reference and insights for the activation of demand-side flexibility potential. Hopefully, this paper can also provide novel insights into the development and further integration of the sharing economy in LEMs.</p

Tunable van Hove singularity without structural instability in Kagome metal CsTi3_3Bi5_5

In Kagome metal CsV$_3$Sb$_5$, multiple intertwined orders are accompanied by both electronic and structural instabilities. These exotic orders have attracted much recent attention, but their origins remain elusive. The newly discovered CsTi$_3$Bi$_5$ is a Ti-based Kagome metal to parallel CsV$_3$Sb$_5$. Here, we report angle-resolved photoemission experiments and first-principles calculations on pristine and Cs-doped CsTi$_3$Bi$_5$ samples. Our results reveal that the van Hove singularity (vHS) in CsTi$_3$Bi$_5$ can be tuned in a large energy range without structural instability, different from that in CsV$_3$Sb$_5$. As such, CsTi$_3$Bi$_5$ provides a complementary platform to disentangle and investigate the electronic instability with a tunable vHS in Kagome metals

Supersolvable orders and inductively free arrangements

In this paper, we define the supersolvable order of hyperplanes in a supersolvable arrangement, and obtain a class of inductively free arrangements according to this order. Our main results improve the conclusion that every supersolvable arrangement is inductively free. In addition, we assert that the inductively free arrangement with the required induction table is supersolvable

TLR7 Agonist-Loaded Gadolinium Oxide Nanotubes Promote Anti-Tumor Immunity by Activation of Innate and Adaptive Immune Responses

Improving the delivery of biomolecules to DCs and lymph nodes is critical to increasing their anti-tumor efficacy, reducing their off-target side effects, and improving their safety. In this study, Gd2O3 nanotubes with lengths of 70–80 nm, diameters of 20–30 nm, and pore sizes of up to 18 nm were synthesized using a facile one-pot solvothermal method. The Gd2O3 nanotubes showed good adsorption capacity of OVA and TLR7a, with a loading efficiency of about 100%. The Gd2O3 nanotubes showed pH-sensitive degradation and biomolecule release properties; the release of gadolinium ions, OVA, and TLR7a was slow at pH 7.4 and fast at pH 5. The Gd2O3 nanotubes showed 2.6–6.0 times higher payload retention around the injection site, 3.1 times higher cellular uptake, 1.7 times higher IL1β secretion, 1.4 times higher TNFα secretion by BMDCs, and markedly enhanced draining lymph node delivery properties. The combination of OVA, TLR7a, and Gd2O3 nanotubes significantly inhibited tumor growth and increased survival rate compared with only OVA-TLR7a, only OVA, and saline. The Gd2O3 nanotubes are biocompatible and can also be used as radiation sensitizers

Sequence and sedimentary features of the Changxing Fm organic reefs and their control on reservoir development in the Yuanba Gas Field, Sichuan Basin

In the Yuanba area, Sichuan Basin, the gas reservoirs in the Upper Permian Changxing Fm are now at the development stage. With the smooth progress of development, it is urgent to characterize the reservoir architectures accurately and summarize the controlling factors for reservoir development. In this paper, research was mainly performed on the Changxing Fm organic reefs in terms of their sequence stratigraphy, sedimentary facies, and reservoir characteristics and architectures based on core observation and thin section analysis, combined with physical property data and logging curves analysis results. It is shown that the Changing Fm can be divided into two third-order sequences and six fourth-order sequences, their electric logs are characterized by abrupt change above and below the high-frequency sequence boundary and are consistent with the sedimentary cycles controlled by high-frequency sequences. Besides, the Changxing Fm organic reefs mainly represents zonal distribution outside SQ2 platform margin, and they are vertically composed of two obvious two reef sedimentary cycles and laterally developed in asymmetric patterns (early in the east and late in the west). Finally, in general, organic reef (bank.) reservoirs are mainly composed of low-porosity and moderate–low-permeability dissolved dolomite reservoirs, and they are mostly distributed at reef caps in the upper–middle parts of the two fourth-order sequences, with the characteristics of multiple beds, thin single beds, different types of reservoirs with different thickness interbedded with each other, strong heterogeneity and double-layer reservoir architectures. It is concluded that the distribution of organic reef microfacies in this area is controlled by high-frequency sequence, which is the key controlling factor for reservoir development and spatial distribution

A nanoscale metal organic frameworks-based vaccine synergises with PD-1 blockade to potentiate anti-tumour immunity

Nanoparticle-based strategies have been proposed to enhance the benefit of cancer immunotherapy. Here the authors show that a cancer vaccine based on metal organic frameworks-gated mesoporous silica nanoparticles for antigen and immune potentiators delivery boosts the therapeutic efficacy of low-dose anti-PD1

Impact of Diets on Response to Immune Checkpoint Inhibitors (ICIs) Therapy against Tumors

Immunotherapy has revolutionized the established therapeutics against tumors. As the major immunotherapy approach, immune checkpoint inhibitors (ICIs) achieved remarkable success in the treatment of malignancies. However, the clinical gains are far from universal and durable, because of the primary and secondary resistance of tumors to the therapy, or side effects induced by ICIs. There is an urgent need to find safe combinatorial strategies that enhance the response of ICIs for tumor treatment. Diets have an excellent safety profile and have been shown to play pleiotropic roles in tumor prevention, growth, invasion, and metastasis. Accumulating evidence suggests that dietary regimens bolster not only the tolerability but also the efficacy of tumor immunotherapy. In this review, we discussed the mechanisms by which tumor cells evade immune surveillance, focusing on describing the intrinsic and extrinsic mechanisms of resistance to ICIs. We also summarized the impacts of different diets and/or nutrients on the response to ICIs therapy. Combinatory treatments of ICIs therapy with optimized diet regimens own great potential to enhance the efficacy and durable response of ICIs against tumors, which should be routinely considered in clinical settings

Investigation of the dynamic response of subgrade vibration compaction based on the finite element method

A three-dimensional finite element model of a vibratory wheel on soil is established though the use of the ABAQUS software platform to investigate the interaction between the wheel and soil and the resulting dynamic response during vibratory compaction. The extended linear Drucker Prager model is used to reflect the plastic deformation characteristics of the soil. The truncated boundary is treated by using a three-dimensional uniform viscoelastic artificial boundary method. The vibratory responses of the soil under the wheel, including the stress and contact force, are analyzed by using numerical simulations. The results show a decrease in the soil vertical stress at the edge of the vibrating wheel transverse to the wheel path, which may assist in identifying the rolling overlap width of the wheel. Along the wheel path, the vertical stress center is demonstrated to lie ahead of the vibrating wheel mass center, caused by the inclination of the wheel soil contact surface. The contact pressure and total grounding width of the soil under the wheel can be calculated by using the finite element method; only one-third of the total width could produce effective compression deformation

An automated machine learning (AutoML) method of risk prediction for decision-making of autonomous vehicles

This study presents a domain-specific automated machine learning (AutoML) for risk prediction and behaviour assessment, which can be used in the behavioural decision-making and motion trajectory planning of autonomous vehicles (AVs). The AutoML enables end-to-end machine learning from vehicle movement and sensing data to detailed risk levels and corresponding behaviour characteristics, which integrates three main components of: unsupervised risk identification by surrogate risk indicators and big data clustering, feature learning based on XGBoost, and model auto-tuning by Bayesian optimisation. Then, the functions and performance of AutoML are evaluated based on NGSIM data, with assumptions of various sensing configurations or data acquisition conditions. AutoML achieves satisfactory results of behaviour-based risk prediction, which has a predictive power of 91.7% overall accuracy for four risk levels, and about 95% accuracy for safe-risk distinction. Bayesian optimisation guides the self-learning of AutoML to get the optimised feature subsets and hyperparameter values. The identification of key features not only produces better performance with fewer computation costs, but also provides data-driven insights about AV design, such as sensor configurations and sensor data mining, from risk decision-making perspectives. The application potentials of AutoML in AVs are discussed.This work was supported in part by the National Natural Science Foundation of China under Grant 61803283 and in part by the National Key Research and Development Program of China under Grant 2018YFB1600500

Key risk indicators for accident assessment conditioned on pre-crash vehicle trajectory

Accident events are generally unexpected and occur rarely. Pre-accident risk assessment by surrogate indicators is an effective way to identify risk levels and thus boost accident prediction. Herein, the concept of Key Risk Indicator (KRI) is proposed, which assesses risk exposures using hybrid indicators. Seven metrics are shortlisted as the basic indicators in KRI, with evaluation in terms of risk behaviour, risk avoidance, and risk margin. A typical real-world chain-collision accident and its antecedent (pre-crash) road traffic movements are retrieved from surveillance video footage, and a grid remapping method is proposed for data extraction and coordinates transformation. To investigate the feasibility of each indicator in risk assessment, a temporal-spatial case-control is designed. By comparison, Time Integrated Time-to-collision (TIT) performs better in identifying pre-accident risk conditions; while Crash Potential Index (CPI) is helpful in further picking out the severest ones (the near-accident). Based on TIT and CPI, the expressions of KRIs are developed, which enable us to evaluate risk severity with three levels, as well as the likelihood. KRI-based risk assessment also reveals predictive insights about a potential accident, including at-risk vehicles, locations and time. Furthermore, straightforward thresholds are defined flexibly in KRIs, since the impact of different threshold values is found not to be very critical. For better validation, another independent real-world accident sample is examined, and the two results are in close agreement. Hierarchical indicators such as KRIs offer new insights about pre-accident risk exposures, which is helpful for accident assessment and prediction.MOE (Min. of Education, S’pore