Atomically precise metal nanoclusters (NCs) represent a promising generation of metal nanomaterial because of characteristic atomic stacking mode, abundant catalytic active sites, and molecular‐like ...discrete energy band structure. However, crafting metal NCs‐dominated photocatalytic systems with mediated charge transport pathways for photoredox catalysis is in the infant stage and their photocatalytic mechanisms remain elusive, which is largely hampered by the ultra‐short charge lifetime, generic instability, and complicated electronic structure of metal NCs. In this study, the smart construction of all‐solid‐state metal NCs‐transition metal chalcogenides quantum dots (TMCs QDs) Z‐scheme artificial photosystems for robust and stable solar‐to‐hydrogen conversion is demonstrated. The concurrent favorable photosensitization efficiency of metal NCs and TMCs QDs synergistically stimulate the unexpected Z‐scheme charge transport pathway, which significantly boosts the anisotropic spatial vectorial charge transport/separation, giving rise to considerably enhanced visible‐light‐responsive photocatalytic hydrogen generation performances along with favorable stability. This study would push forward the prosperity of exploring metal NCs‐based photosystems for solar‐to‐hydrogen conversion.
Atomically precise metal nanoclusters mediated Z‐scheme photosystems are for the first time exquisitely crafted for solar‐to‐hydrogen conversion. The favorable energy level alignment among Au25(GSH)18 nanoclusters, CdSe QDs, and MoSe2 along with elegant interface configuration modulation facilitates the directional charge migration and separation, giving rise to high‐performance Z‐scheme photosystem for photocatalytic hydrogen generation under visible light irradiation.
In December 2019, a novel coronavirus (2019-nCoV) caused an outbreak in Wuhan, China, and soon spread to other parts of the world. It was believed that 2019-nCoV was transmitted through respiratory ...tract and then induced pneumonia, thus molecular diagnosis based on oral swabs was used for confirmation of this disease. Likewise, patient will be released upon two times of negative detection from oral swabs. However, many coronaviruses can also be transmitted through oral-fecal route by infecting intestines. Whether 2019-nCoV infected patients also carry virus in other organs like intestine need to be tested. We conducted investigation on patients in a local hospital who were infected with this virus. We found the presence of 2019-nCoV in anal swabs and blood as well, and more anal swab positives than oral swab positives in a later stage of infection, suggesting shedding and thereby transmitted through oral-fecal route. We also showed serology test can improve detection positive rate thus should be used in future epidemiology. Our report provides a cautionary warning that 2019-nCoV may be shed through multiple routes.
In this paper, a distributed filtering scheme is presented to deal with the fault detection problem of nonlinear stochastic systems with wireless sensor networks (WSNs). The nonlinear stochastic ...systems, which are of discrete-time form, are represented by interval type-2 (IT2) Takagi-Sugeno (T-S) fuzzy models. Each sensor of the WSN can receive measurements from itself and its neighboring sensors subject to a deterministic interconnection topology. Independent random variables obeying the Bernoulli distribution are formulated to characterize the randomly occurred packet losses between the WSN and the filter unit. To generate residual signals for evaluation functions of the fault detection mechanism, a novel type of IT2 T-S fuzzy distributed fault detection filter is proposed corresponding to each sensor node. Additionally, a fault reference model is adopted for improving the performance of the fault detection system. A new overall fault detection system is formulated in an IT2 T-S fuzzy model framework. Applying Lyapunov functional approach, we concentrate on the analysis of stability and performance of the resulting fault detection system. New techniques are utilized to handle the decoupling problem in design procedure. The desired parametric matrices of the fuzzy filters are designed subject to a developed criterion, which is a sufficient condition of the robust mean-square asymptotic stability for the overall fault detection system with a disturbance attenuation performance. Finally, a truck-trailer system with a four-node WSN is established for simulation validation. In simulations, the mincx function of the MatLab 2017a in Windows 10 OS is used to optimize the level of the disturbance attenuation performance, and to obtain the filter gains for the established system. By comparing the different time instants when the residual evaluation functions exceed their respective thresholds, simulation results successfully validate the effectiveness and applicability of the presented distributed fault detection scheme.
Activated macrophages switch from oxidative phosphorylation to aerobic glycolysis, similar to the Warburg effect, presenting a potential therapeutic target in inflammatory disease. The endogenous ...metabolite itaconate has been reported to regulate macrophage function, but its precise mechanism is not clear. Here, we show that 4-octyl itaconate (4-OI, a cell-permeable itaconate derivative) directly alkylates cysteine residue 22 on the glycolytic enzyme GAPDH and decreases its enzyme activity. Glycolytic flux analysis by U
C glucose tracing provides evidence that 4-OI blocks glycolytic flux at GAPDH. 4-OI thereby downregulates aerobic glycolysis in activated macrophages, which is required for its anti-inflammatory effects. The anti-inflammatory effects of 4-OI are replicated by heptelidic acid, 2-DG and reversed by increasing wild-type (but not C22A mutant) GAPDH expression. 4-OI protects against lipopolysaccharide-induced lethality in vivo and inhibits cytokine release. These findings show that 4-OI has anti-inflammatory effects by targeting GAPDH to decrease aerobic glycolysis in macrophages.
The rise of proximity tourism since the onset of COVID‐19 has enabled individuals to assess their daily lives from new perspectives. Nevertheless, in studies of this change, scant attention has been ...given to the influence of government performance on the perception and behavior of proximity tourists. Drawing upon micro‐governance theory in conjunction with the two‐factor hygiene model, this study proposes a novel conceptual framework. The effects of pandemic‐related strict vs. relaxed travel restrictions on potential proximity tourists' intention to travel are examined alongside mortality salience as a moderator and perceived extraordinariness as a mediator. Two quasi‐experiments reveal a positive link between government performance and travel intention, especially when moderated by mortality salience. Relaxed travel restrictions, coupled with the performance of government protests, greater perceived extraordinariness, thus amplifying travel intention. This study offers pioneering empirical validation of government response and its impact on post‐disaster proximity tourism intention.
•Deep learning-based methods are developed for building cooling load prediction.•Accurate predictions on 24-h ahead building cooling load profiles are achieved.•The potentials of supervised and ...unsupervised deep learning is investigated.•Features extracted by unsupervised deep learning can improve model performance.•Supervised deep learning does not show obvious advantages in model development.
Short-term building cooling load prediction is the essential foundation for many building energy management tasks, such as fault detection and diagnosis, demand-side management and control optimization. Conventional methods, which heavily rely on physical principles, have limited power in practice as their performance is subject to many physical assumptions. By contrast, data-driven methods have gained huge interests due to their flexibility in model development and the rich data available in modern buildings. The rapid development in data science has provided advanced data analytics to tackle prediction problems in a more convenient, efficient and effective way.
This paper investigates the potential of one of the most promising techniques in advanced data analytics, i.e., deep learning, in predicting 24-h ahead building cooling load profiles. Deep learning refers to a collection of machine learning algorithms which are powerful in revealing nonlinear and complex patterns in big data. Deep learning can be used either in a supervised manner to develop prediction models with given inputs and output (i.e., cooling load), or in an unsupervised manner to extract meaningful features from raw data as model inputs. This study exploits the potential of deep learning in both manners, and compares its performance in cooling load prediction with typical feature extraction methods and popular prediction techniques in the building field. The results show that deep learning can enhance the performance of building cooling load prediction, especially when used in an unsupervised manner for constructing high-level features as model inputs. Using the features extracted by unsupervised deep learning as inputs for cooling load prediction can evidently enhance the prediction performance. The findings are enlightening and could bring more flexible and effective solutions for building energy predictions.
Atherosclerosis is a chronic inflammatory vascular disease driven by traditional and nontraditional risk factors. Genome-wide association combined with clonal lineage tracing and clinical trials have ...demonstrated that innate and adaptive immune responses can promote or quell atherosclerosis. Several signaling pathways, that are associated with the inflammatory response, have been implicated within atherosclerosis such as NLRP3 inflammasome, toll-like receptors, proprotein convertase subtilisin/kexin type 9, Notch and Wnt signaling pathways, which are of importance for atherosclerosis development and regression. Targeting inflammatory pathways, especially the NLRP3 inflammasome pathway and its regulated inflammatory cytokine interleukin-1β, could represent an attractive new route for the treatment of atherosclerotic diseases. Herein, we summarize the knowledge on cellular participants and key inflammatory signaling pathways in atherosclerosis, and discuss the preclinical studies targeting these key pathways for atherosclerosis, the clinical trials that are going to target some of these processes, and the effects of quelling inflammation and atherosclerosis in the clinic.
Modern buildings are expected to be not only energy efficient but also energy flexible to facilitate reliable integration of intermittent renewable energy sources into smart grids. Estimating the ...aggregate energy-flexibility potential at a cluster level plays a key role in assessing financial benefits and service area for energy-flexibility services at design stage and determining real-time pricings at operating stage. However, most existing studies focused on the energy flexibility of individual buildings rather than building clusters. In addition, due to the intrinsic uncertainty in building envelope parameters, performance of building energy systems, and occupancy and occupant behavior, it is necessary to quantify the uncertainty in aggregate energy flexibility. In this study, we developed an approach to quantifying the uncertainty in the aggregate energy flexibility of residential building clusters using a data-driven stochastic occupancy model that can capture the stochasticity of occupancy patterns. A questionnaire survey was carried out to collect occupancy time-series data in Hong Kong for occupancy model identification. Aggregation analysis was conducted considering various building archetypes and occupancy patterns. The uncertainty in aggregate energy flexibility was then quantified based on the proposed performance indices using Monte Carlo technique. With the scaling up of building clusters, the estimated energy-flexibility potential became steady and the weekly energy flexibility stayed around 12.40%. However, the weekly uncertainty of aggregated energy flexibility exponentially decreased from 19.12% for 8 households to 0.74% for 5120 households, which means that the estimate of a building cluster’s energy flexibility is more reliable than that of a single building.
•A stochastic occupancy model was developed using questionnaire survey data.•Aggregate energy flexibility of residential building clusters was investigated.•Performance indices were proposed to quantify uncertainty in energy flexibility.•Uncertainty in aggregate energy flexibility of building clusters was quantified.
Since its emergence in December 2019, corona virus disease 2019 (COVID-19) has impacted several countries, affecting more than 90 thousand patients and making it a global public threat. The routes of ...transmission are direct contact, and droplet and possible aerosol transmissions. Due to the unique nature of dentistry, most dental procedures generate significant amounts of droplets and aerosols, posing potential risks of infection transmission. Understanding the significance of aerosol transmission and its implications in dentistry can facilitate the identification and correction of negligence in daily dental practice. In addition to the standard precautions, some special precautions that should be implemented during an outbreak have been raised in this review.
It is still a challenging project for developing electromagnetic (EM) wave absorption materials with the properties of multi-band absorption in the field of microwave absorption. Herein, ...honeycomb-like MoSe2@reduced graphene oxide (RGO) composites are successfully constructed by a facile one-step solvothermal method. The polarization and conductive loss can be precisely tailored by vertically implanting MoSe2 nanosheets on the RGO sheet. It is notable that three-band absorption, including S, X, and Ku bands, was achieved in the composites. The optimized MoSe2@RGO composite exhibits excellent microwave absorption performance, the maximum reflection loss (RL) value can reach −56.9 dB and an effective absorption bandwidth (EAB) value can reach 4.12 GHz. The excellent microwave attenuation capability is attributed to the synergy effect of conductive loss, multiple relaxations, and excellent impedance matching in the composites. Therefore, this work provides a new paradigm for developing efficient multifunctional EM wave absorbers.
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•The growth mechanism of MoSe2@RGO composites by vertically implanting MoSe2 nanosheets distributed on RGO was clarified.•The tunable electromagnetic properties were revealed.•Multi-band microwave absorption with S, C, and Ku bands was realized.•The multiple EM loss mechanisms in the composite were discussed in detail.
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