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Digital Twins promise to deliver a step-change in distribution system operations and planning, but there are few real-world examples that explore the challenges of combining imperfect model and measurement data, and then use these as the basis for subsequent analysis. In this work we propose a Digital Twin framework for electrical distribution systems and implement that framework on the Smart Energy Network Demonstrator microgrid in the UK. The data and software implementation are made available open-source, and consist of a network model, power meter measurements, and unbalanced power flow-based algorithms. Measurement and network uncertainties are shown to have a substantial impact on the quality of Digital Twin outputs. The potential benefits of a dynamic export limit and voltage control are estimated using the Digital Twin, using simulated measurements to address data quality challenges, with results showing curtailment for an exemplar day could be reduced by 56%. Power meter data and a network model are shown to be necessary for developing algorithms that enable decision-making that is robust to real-world uncertainties, with possibilities and challenges of Digital Twin development clearly demonstrated.

Thermal emission caused by the thermal motion of the charged particles is commonly broadband, un-polarized, and incoherent, like a melting pot of electromagnetic waves, which makes it unsuitable for infrared applications in many cases requiring specific thermal emission properties. Metasurfaces, characterized by two-dimensional subwavelength artificial nanostructures, have been extensively investigated for their flexibility in tuning optical properties, which provide an ideal platform for shaping thermal emission. Recently, remarkable progress was achieved not only in tuning thermal emission in multiple degrees of freedom, such as wavelength, polarization, radiation angle, coherence, and so on but also in applications of compact and integrated optical devices. Here, we review the recent advances in the regulation of thermal emission through metasurfaces and corresponding infrared applications, such as infrared sensing, radiative cooling, and thermophotovoltaic devices.

Abstract Realgar-Indigo naturalis formula (RIF), an oral traditional Chinese medicine mainly containing Realgar (As4S4), is highly effective in treating adult acute promyelocytic leukemia (APL). However, the treatment efficacy and safety of RIF have not been verified in pediatric patients. SCCLG-APL group conducted a multicenter randomized non-inferiority trial to determine whether intravenous arsenic trioxide (ATO) can be substituted by oral RIF in treating pediatric APL. Of 176 eligible patients enrolled, 91 and 85 were randomized to ATO and RIF groups, respectively. Patients were treated with the risk-adapted protocol. Induction, consolidation, and 96-week maintenance treatment contained all-trans-retinoic acid and low-intensity chemotherapy, and either ATO or RIF. The primary endpoint was 5-year event-free survival (EFS). The secondary endpoints were adverse events and hospital days. After a median 6-year follow-up, the 5-year EFS was 97.6% in both groups. However, the RIF group had significantly shorter hospital stays and lower incidence of infection and tended to have less cardiac toxicity. All 4 relapses occurred within 1.5 years after completion of maintenance therapy. No long-term arsenic retentions were observed in either group. Substituting oral RIF for ATO maintains treatment efficacy while reducing hospitalization and adverse events in treating pediatric APL patients, which may be a future treatment strategy for APL.

Background: Pediatric acute myeloid leukemia (AML) has poor prognosis and high rate of relapse and mortality, and exploration of new treatment options is still critically needed. Objectives: To summarize the outcome of our new treatment strategies for pediatric AML, which is characterized by dual induction and acute lymphoblastic leukemia (ALL) elements consolidation. Design: Retrospective, single-arm study. Methods: From July 2012 to December 2019, an intensive chemotherapy protocol was used for newly diagnosed children with AML, which contains dual induction, three courses of consolidations based on high-dose cytarabine, and two courses of consolidations composed of high-dose methotrexate, vincristine, asparaginase, and mercaptopurine (ALL-like elements). Blasts were monitored by bone marrow smears at intervals, and two lumbar punctures were performed during chemotherapy. We retrospectively analyzed the efficacy and safety of this study. The last follow-up was on 26 May 2023. Results: A total of 70 pediatric AMLs were included. The median age at diagnosis was 6.7 (0.5–16.0) years. The median initial WBC count was 23.74 × 10 9 /L, 11 of whom ⩾100 × 10 9 /L. After dual induction, there were 62 cases of complete remission (CR), 5 cases of partial remission, and 3 cases of nonremission. The CR rate was 88.57%. The median follow-up time was 5.8 (0.2–9.4) years, the 5-year overall survival was 78.2% ± 5%, the event-free survival (EFS) was 71.2% ± 5.6%, and the cumulative recurrence rate was 27.75%. The 5-year EFS of patients with initial WBC

Background: Acute myeloid leukemia (AML) is a type of blood cancer characterized by excessive growth of immature myeloid cells. Unfortunately, the prognosis of pediatric AML remains unfavorable. It is imperative to further our understanding of the mechanisms underlying leukemogenesis and explore innovative therapeutic approaches to enhance overall disease outcomes for patients with this condition. Methods: Quantitative reverse-transcription PCR was used to quantify the expression levels of microRNA (miR)-133a and miR-135a in 68 samples from 59 pediatric patients with AML. Dual-luciferase reporter transfection assay, Cell Counting Kit-8 assay, and western blot analysis were used to investigate the functions of miR-133a and miR-135a. Results: Our study found that all-trans-retinoic acid (ATRA) promoted the expression of miR-133a and miR-135a in AML cells, inhibited caudal type homeobox 2 (CDX2) expression, and subsequently inhibited the proliferation of AML cells. Additionally, miR-133a and miR-135a were highly expressed in patients with complete remission and those with better survival. Conclusions: miR-133a and miR-135a may play an antioncogenic role in pediatric AML through the ATRA-miRNA133a/135a-CDX2 pathway. They hold promise as potentially favorable prognostic indicators and novel therapeutic targets for pediatric AML.

Background Acute myeloid leukemia (AML) with Fms-like tyrosine kinase 3 gene internal tandem duplication (FLT3-ITD) mutations has a poor prognosis. The combination of arsenic trioxide (ATO) and all-trans retinoic acid (ATRA) has a synergistic killing effect on leukemia cells with FLT3-ITD mutation. However, the mechanism, especially the changes of gene expression and metabolic activity remain unclear. Here we explore the transcriptome and metabolomics changes of FLT3-ITD AML cells treated with ATO/ATRA. Methods RNA-seq was used to identify differential expressed genes (DEGs), and ultra-high performance liquid chromatography-quadrupole electrostatic field orbital trap mass spectrometry (UHPLC-QE-MS) nontargeted metabolomics method was used to screen out the differential metabolites in FLT3-ITD mutant cell lines treated with ATRA and ATO. KEGG pathway database was utilized for pathway exploration and Seahorse XF24 was used to detect extracellular acidification rate (ECAR). Metabolic polymerase chain reaction (PCR) array and real-time quantitative PCR (RT-qPCR) were used to detect mRNA levels of key metabolic genes of glycolysis and fatty acid after drug treatment. Results A total of 3873 DEGs were identified and enriched in 281 Gene Ontology (GO) terms, among which 210 were related to biological processes, 43 were related to cellular components, and 28 were related to molecular functions. Besides, 1794 and 927 differential metabolites were screened in positive and negative ion mode separately, and 59 different metabolic pathways were involved, including alanine-aspartate-glutamate metabolic pathway, arginine, and proline metabolic pathway, glycerophospholipid metabolic pathways, etc. According to KEGG Pathway analysis of transcriptome combined with metabolome, glycolysis/gluconeogenesis pathway and fatty acid metabolism pathway were significantly founded enriched. ATRA + ATO may inhibit the glycolysis of FLT3-ITD AML cells by inhibiting FLT3 and its downstream AKT/HK2-VDAC1 signaling pathway. Conclusions The gene transcription profile and metabolites of FLT3-ITD mutant cells changes significantly after treatment, which might be related to the anti-FLT3-ITD AML effect. The screened DEGs, differential metabolites pathway are helpful in studying the mechanism of anti-leukemia effects and drug targets.

Abstract The number of clinical trials conducted in mainland China, including investigator‐initiated trials (IITs), has increased rapidly in recent years. However, there are few data on the characteristics of cancer‐related IITs. We performed a comprehensive analysis of the landscape of cancer‐related IITs in mainland China in the past decade. All cancer‐related IITs registered on two clinical trial registries in the United States (www.clinicaltrials.gov, CT.gov) and mainland China (www.chictr.org.cn, ChiCTR) from 2010 to 2019 were identified. IITs were reviewed manually to validate classification, subcategorized by cancer type, and stratified by design characteristics to facilitate comparison across cancer types and with other specialties. A total of 8199 cancer‐related IITs were identified. The number of trials registered annually increased over time, especially in the last 5 years. Although interventional studies were predominant, randomized double‐blind studies accounted for only 8% of IITs. In the past decade, the trend for interventional studies conducted with different drugs increased year on year, although the increase in hormonal therapy IITs was not significant. Additionally, cancer‐related IITs were unevenly geographically distributed, with half concentrated in the economically developed cities Shanghai, Beijing, and Guangdong. We also found an increase in registration before participant enrollment (64.9% for trials in conducted in 2015–2019 vs. 40.2% in 2010–2014, p

To decarbonize the energy system by the year 2050, it is crucial that innovations are trialled in a ‘real world’ setting for the purpose of increasing public adoption and support, and for providing insights to decision-makers to ensure their decisions are effective and influential. Together, renewable energy systems, distributed and digitized ‘smart’ energy networks (SEN) provide opportunities to maximize energy efficiency, reduce transmission losses and drive down greenhouse gas emissions. Yet, such integrated Smart Local Energy Systems (SLES) are in the early stages of development and the technologies that underpin them lack testbeds where they can be developed and tested in a real-world environment. Here we demonstrate the potential role of one of Europe’s largest ‘at scale’ multi-vector Smart Energy Network Demonstrator—SEND, developed within a ‘living laboratory’ setting that provides the ‘blueprint’ for the development and testing of low-carbon energy technologies on the UK’s journey to net zero. Based on the SEND platform and data, we have developed and demonstrated several novel AI based smart algorithms for intelligent SLES control and management. We are also working with industry partners to develop a digital twin of the smart energy system on our campus.

During the process of the shear and bending failure of reinforced concrete beams, the cracks generated on the side of the beam are mostly type I–II composite cracks. Considering the material nonlinearity and geometric irregularity of reinforced concrete, it is of great significance to study the dynamic evolution law of crack propagation in reinforced concrete members. Crack propagation consists of the elongation of the crack length at the surface of the concrete material, as well as the widening of the crack width. In this paper, a finite element model of simply supported reinforced concrete beams is established by considering material nonlinearity and geometric nonlinearity and considering the damage of the concrete. Comparing the experimental results, the finite element model can accurately reflect the strain changes of longitudinal reinforcement and stirrups with a maximum error within 10%. The crack development of concrete is basically consistent with the experimental results. It shows the accuracy of the finite element analysis. On this basis, a beam–column joint is analyzed and its crack propagation under reciprocating load is investigated. An important contribution is made to the preliminary analysis of the actual crack control project. The nonlinear finite element results show that the compressive damage to the concrete of the joint is insignificant at 0.1. but the tensile damage is significant at 0.96. The cracks in the connection between the frame and the floor slab mainly show bending cracks and the concrete damage in the floor slab is higher than that in the beam.

Abstract Smart local energy system (SLES) can support tailored regional solutions through the orchestration of cyber physical architectures, coordinating distributed technologies, with operational and forecasting models across all energy actors. Unprecedented access to new information, data streams and remotely accessible control can substantially benefit the multi-objective optimisation of multiple performance metrics. Given the expansion of this internet of things (IoT) and cyber-physical system (CPS), it is important to not only design effective detection and management of potential cybersecurity issues, but also to address the challenges in having affective and adaptive governance—built on standards to ensure the security of the IoT to minimise risks and harms to all users. This study conducts an extensive and critical investigation into the existing standards and identifies areas to focus on as to support the expansive adoption of cyber physical networks. Although existing standards and protocols are highly fragmented, our findings suggest that many of them can meet the requirements of the applications and infrastructures of SLES. Additionally, many standards have been introduced to protect information security and personal privacy due to their increasing importance. The research also suggests that the industry needs to produce more affordable and cyber-secured devices and services. For the government and regulators, relevant guidelines on the minimum function and security requirements for applications should be provided. Additionally, compliance testing and certifications should be in place and carried out by an independent third party to ensure the components of SLES ecosystem with a satisfied security level by design.

Smart energy networks provide an effective means to accommodate high penetrations of variable renewable energy sources like solar and wind, which are key for the deep decarbonisation of energy production. However, given the variability of the renewables as well as the energy demand, it is imperative to develop effective control and energy storage schemes to manage the variable energy generation and achieve desired system economics and environmental goals. In this paper, we introduce a hybrid energy storage system composed of battery and hydrogen energy storage to handle the uncertainties related to electricity prices, renewable energy production, and consumption. We aim to improve renewable energy utilisation and minimise energy costs and carbon emissions while ensuring energy reliability and stability within the network. To achieve this, we propose a multi-agent deep deterministic policy gradient approach, which is a deep reinforcement learning-based control strategy to optimise the scheduling of the hybrid energy storage system and energy demand in real time. The proposed approach is model-free and does not require explicit knowledge and rigorous mathematical models of the smart energy network environment. Simulation results based on real-world data show that (i) integration and optimised operation of the hybrid energy storage system and energy demand reduce carbon emissions by 78.69%, improve cost savings by 23.5%, and improve renewable energy utilisation by over 13.2% compared to other baseline models; and (ii) the proposed algorithm outperforms the state-of-the-art self-learning algorithms like the deep-Q network.

Load forecasting is an essential task performed within the energy industry to help balance supply with demand and maintain a stable load on the electricity grid. As supply transitions towards less reliable renewable energy generation, smart meters will prove a vital component to facilitate these forecasting tasks. However, smart meter adoption is low among privacy-conscious consumers that fear intrusion upon their fine-grained consumption data. In this work we propose and explore a federated learning (FL) based approach for training forecasting models in a distributed, collaborative manner whilst retaining the privacy of the underlying data. We compare two approaches: FL, and a clustered variant, FL+HC against a non-private, centralised learning approach and a fully private, localised learning approach. Within these approaches, we measure model performance using RMSE and computational efficiency. In addition, we suggest the FL strategies are followed by a personalisation step and show that model performance can be improved by doing so. We show that FL+HC followed by personalisation can achieve a ~5% improvement in model performance with a ~10x reduction in computation compared to localised learning. Finally we provide advice on private aggregation of predictions for building a private end-to-end load forecasting application.

The aim of this study was to examine the effective length factor of frame–core wall structures with cross-layer columns, which are relevant for current high-rise building construction. Using the finite element method, improved inflection point method (D-value method), and GB50017-2017, the study investigated how the height and distribution of cross-layer columns affect the lateral stiffness ratio, natural vibration period, member internal force, maximum interlayer displacement angle, and effective length factor of the column in the frame–core wall structures. However, the force acting in the frame in the weak axis direction that is considered in GB50017-2017 does not reflect the actual mechanical behavior. Therefore, when determining the effective length factor of cross-layer columns, the interaction between the remaining frames’ sub-structure and cross-layer columns is considered and the effective length factor is modified accordingly. A simplified model of a 140 m frame–core wall structure was established for analysis, and it was assumed that rigid links connect the frame and core wall hinged at both ends. The results show that increasing the height and number of cross-layer columns decreased the lateral stiffness ratio of the structure, and increased the maximum interlayer displacement angle and natural vibration period. Furthermore, the effective length factor of the structure decreased with an increase in height and the number of cross-layer columns. The modified effective length factor agrees well with the results obtained by the finite element method. These findings provide a useful reference for calculating the load-carrying capacity of cross-layer columns in engineering.

In this paper, organosilicone-modified polyurethane was synthesized by the prepolymerization method by using polypropylene glycol and isophorone diisocyanate as the synthetic monomers, and γ-aminopropyltriethoxysilane (KH550) as the silicone source. The modified polyurethane was used as the interlayer to prepare the organic-inorganic glass laminates. The effects of silicone contents on the optical properties, thermomechanical properties, and interfacial adhesion property of the glass laminates were investigated. With the KH550 content increasing, the polymerization degree of polyurethane is decreased, resulting in lower transparency, higher haze and lower surface hardness. The storage modulus of the modified polyurethane is increased at first and then decreased, and the glass transition temperature of hard segments is firstly increased and then decreased. Both reached the maximum value when the additive amount of KH550 is 1%. Using polyurethane adhesive layer as the interlayer of glass laminates, the interfacial shear strength of the unmodified glass laminates is 6.7 MPa, while the interfacial shear strength of glass laminates containing 0.5% KH550 is 7.7 MPa.

Digital Twin technology is an emerging concept that has become the centre of attention for industry and, in more recent years, academia. The advancements in industry 4.0 concepts have facilitated its growth, particularly in the manufacturing industry. The Digital Twin is defined extensively but is best described as the effortless integration of data between a physical and virtual machine in either direction. The challenges, applications, and enabling technologies for Artificial Intelligence, Internet of Things (IoT) and Digital Twins are presented. A review of publications relating to Digital Twins is performed, producing a categorical review of recent papers. The review has categorised them by research areas: manufacturing, healthcare and smart cities, discussing a range of papers that reflect these areas and the current state of research. The paper provides an assessment of the enabling technologies, challenges and open research for Digital Twins.

Objective To explore the treatment of difficult-to-wean patients with glycogen storage disease type Ⅱ (GSDⅡ) complicated with respiratory failure. Methods A total of 6 patients with GSDⅡ (3 children and 3 infants) who were admitted due to weak respiratory muscle and had difficulty in weaning off ventilator were treated in our hospital from October 2015 to September 2017. They received comprehensive treatment of antibiotics for ventilator-associated pneumonia, encouraging voluntary expectoration, respiratory muscle exercises, nutrition strengthening, psychological support, and enzyme replacement treatment (ERT) by using recombinant human acid α-glucosidase [rhGAA, 20 mg/(kg·time), once/biweekly]. These patients gradually transformed from invasive ventilator at biphasic positive airway pressure (BiPAP) mode and continuous positive airway pressure (CPAP) mode to non-invasive ventilator and high flow oxygen therapy. Results The respiratory function and limb muscle strength of all patients were improved after ERT and sequential weaning. With comprehensive treatment, 3 patients weaned off ventilator, one patient underwent endotracheal intubation, and 2 patients died. Conclusions With comprehensive treatment of preventing ventilator-associated pneumonia, respiratory muscle exercises, nutrition strengthening, and ERT using rhGAA, the respiratory function and limb muscle strength were improved in GSD Ⅱ patients. Programmed weaning procedure increases the treatment success rate of difficult-to-wean patients. DOI: 10.3969/j.issn.1672-6731.2019.06.004

To study the vibration characteristics of the poppet valve induced by cavitation, the signal analysis method based on the ensemble empirical mode decomposition (EEMD) method was studied experimentally. The component induced by cavitation was separated from the vibration signals through the EEMD method. The results show that the IMF2 component has the largest amplitude and energy of all components. The root mean square (RMS) value, peak value of marginal spectrum, and center frequency of marginal spectrum of the IMF2 component were studied in detail. The RMS value and the peak value of the marginal spectrum decrease with a decrease of cavitation intensity. The center frequency of marginal spectrum is between 12 kHz and 20 kHz, and the center frequency first increases and then decreases with a decrease of cavitation intensity. The change rate of the center frequency also decreases with an increase of inlet pressure.

Electric vehicles (EVs) provide environmentally friendly transport and they are considered to be an important component of distributed and mobile electric energy storage and supply system. It is possible that EVs can be used to store and transport energy from one geographical area to another as a supportive energy supply. Electricity consumption management should consider carefully the inclusion of EVs. One critical challenge in the consumption management for EVs is the optimization of battery charging. This paper provides a dynamic game theoretic optimization framework to formulate the optimal charging problem. The optimization considers a charging scenario where a large number of EVs charge simultaneously during a flexible period of time. Based on stochastic mean field game theory, the optimization will provide an optimal charging strategy for the EVs to proactively control their charging speed in order to minimize the cost of charging. Numerical results are presented to demonstrate the performance of the proposed framework.

The Smart Meter Rollout Programme in the UK has required energy suppliers to offer new smart meters to customers to provide near real-time energy use information and enable two-way communication between the meter and the central system. The provision was expected to result in meaningful energy reductions, but recent estimates suggest that these reductions may be as low as 2%. This paper contributes to the ongoing debate about the effectiveness of smart meters and in-home energy displays by providing insights on energy feedback perceptions from a series of focus groups with postgraduate consumers. In addition to domestic energy use, the study investigated how participants perceived their energy use at work and how they perceived the energy reduction efforts of their institutions and employers. A laddered and projective methodology was used to more deeply question participant perceptions and reveal their attitudes. The analysis of responses revealed a limited awareness around energy efficiency strategies and opportunities for more visual, mobile, engaging and target-driven interfaces for energy data. The findings also agree with previous observations that environmental concerns are not a key driver of energy reduction behaviours. This was shown by laddered questioning, not to be due to a lack of environmental concern, but rather the perception that reducing energy consumption would have negligible impact. A decade after in-home energy displays enabled a means of providing ‘visibility’ to ‘invisible’ energy consumption, little appears to have changed in the perception and experience of energy feedback.

Rule induction is a practical approach to knowledge discovery. Provided that a problem is developed, rule induction is able to return the knowledge that addresses the goal of this problem as if-then rules. The primary goals of knowledge discovery are for prediction and description. The rule format knowledge representation is easily understandable so as to enable users to make decisions. This paper presents the potential of rule induction for energy efficiency. In particular, three rule induction techniques are applied to derive knowledge from a dataset of thousands of Irish electricity customers' time-series power consumption records, socio-demographic details, and other information, in order to address the following four problems: 1) discovering mathematically interesting knowledge that could be found useful; 2) estimating power consumption features for customers, so that personalized tariffs can be assigned; 3) targeting a subgroup of customers with high potential for peak demand shifting; and 4) identifying customer attitudes that dominate energy conservation.

This paper proposes a novel solution, called a decentralized, efficient, privacy-preserving, and selective aggregation (DEP2SA) scheme, designed to support secure and user privacy-preserving data collection in the advanced metering infrastructure. DEP2SA is more efficient and applicable in real-life deployment, as compared with the state of the art, by adopting and adapting a number of key technologies: 1) it uses a multi-recipient system model, making it more applicable to a liberalized electricity market; 2) it uses the homomorphic Paillier encryption and selective aggregation methods to protect users' consumption data against both external and internal attacks, thus making it more secure; 3) it aggregates data at the gateways that are closest to the data originator, thus saving bandwidth and reducing the risk of creating a performance bottleneck in the system; and 4) it uses short signature and batch signature verification methods to further reduce computational and communication overheads imposed on aggregating nodes. The scheme has been analyzed in terms of security, computational, and communication overheads, and the results show that it is more secure, efficient, and scalable than related schemes.

We present some of the ongoing standardisation work in M2M communications followed by the application of machine-to-machine (M2M) communications to smart grid. We analyse and discuss the enabling technologies in M2M and present an overview of the communications challenges and research opportunities with a focus on wireless sensor networks and their applications in a smart grid environment.