The anode oxygen evolution reaction (OER) is known to largely limit the efficiency of electrolyzers owing to its sluggish kinetics. While crystalline metal oxides are promising as OER catalysts, ...their amorphous phases also show high activities. Efforts to produce amorphous metal oxides have progressed slowly, and how an amorphous structure benefits the catalytic performances remains elusive. Now the first scalable synthesis of amorphous NiFeMo oxide (up to 515 g in one batch) is presented with homogeneous elemental distribution via a facile supersaturated co‐precipitation method. In contrast to its crystalline counterpart, amorphous NiFeMo oxide undergoes a faster surface self‐reconstruction process during OER, forming a metal oxy(hydroxide) active layer with rich oxygen vacancies, leading to superior OER activity (280 mV overpotential at 10 mA cm−2 in 0.1 m KOH). This opens up the potential of fast, facile, and scale‐up production of amorphous metal oxides for high‐performance OER catalysts.
Amorphous NiFeMo oxide (up to 515 g one batch) with homogeneous elemental distribution was synthesized through a facile supersaturated co‐precipitation method. The amorphous NiFeMo oxide undergoes rapid surface self‐reconstruction during OER that forms a metal oxy(hydroxide) active layer with oxygen vacancies, enabling efficient OER catalysis.
Hydroxide exchange membrane fuel cells offer possibility of adopting platinum-group-metal-free catalysts to negotiate sluggish oxygen reduction reaction. Unfortunately, the ultrafast hydrogen ...oxidation reaction (HOR) on platinum decreases at least two orders of magnitude by switching the electrolytes from acid to base, causing high platinum-group-metal loadings. Here we show that a nickel-molybdenum nanoalloy with tetragonal MoNi
phase can catalyze the HOR efficiently in alkaline electrolytes. The catalyst exhibits a high apparent exchange current density of 3.41 milliamperes per square centimeter and operates very stable, which is 1.4 times higher than that of state-of-the-art Pt/C catalyst. With this catalyst, we further demonstrate the capability to tolerate carbon monoxide poisoning. Marked HOR activity was also observed on similarly designed WNi
catalyst. We attribute this remarkable HOR reactivity to an alloy effect that enables optimum adsorption of hydrogen on nickel and hydroxyl on molybdenum (tungsten), which synergistically promotes the Volmer reaction.
The smart community (SC), as an important part of the Internet of Energy (IoE), can facilitate integration of distributed renewable energy sources and electric vehicles (EVs) in the smart grid. ...However, due to the potential security and privacy issues caused by untrusted and opaque energy markets, it becomes a great challenge to optimally schedule the charging behaviors of EVs with distinct energy consumption preferences in SC. In this paper, we propose a contract-based energy blockchain for secure EV charging in SC. First, a permissioned energy blockchain system is introduced to implement secure charging services for EVs with the execution of smart contracts. Second, a reputation-based delegated Byzantine fault tolerance consensus algorithm is proposed to efficiently achieve the consensus in the permissioned blockchain. Third, based on the contract theory, the optimal contracts are analyzed and designed to satisfy EVs' individual needs for energy sources while maximizing the operator's utility. Furthermore, a novel energy allocation mechanism is proposed to allocate the limited renewable energy for EVs. Finally, extensive numerical results are carried out to evaluate and demonstrate the effectiveness and efficiency of the proposed scheme through comparison with other conventional schemes.
Mobile edge computing (MEC) is a new paradigm to alleviate resource limitations of mobile IoT networks through computation offloading with low latency. This article presents an efficient and secure ...multi-user multi-task computation offloading model with guaranteed performance in latency, energy, and security for mobile-edge computing. It does not only investigate offloading strategy but also considers resource allocation, compression and security issues. Firstly, to guarantee efficient utilization of the shared resource in multi-user scenarios, radio and computation resources are jointly addressed. In addition, JPEG and MPEG4 compression algorithms are used to reduce the transfer overhead. To fulfill security requirements, a security layer is introduced to protect the transmitted data from cyber-attacks. Furthermore, an integrated model of resource allocation, compression, and security is formulated as an integer nonlinear problem with the objective of minimizing the weighted sum of energy under a latency constraint. As this problem is considered as NP-hard, linearization and relaxation approaches are applied to transform the problem into a convex one. Finally, an efficient offloading algorithm is designed with detailed processes to make the computation offloading decision for computation tasks of mobile users. Simulation results show that our model not only saves about 46% of system overhead consumption in comparison with local execution but also scale well for large-scale IoT networks.
In cloud computing, high energy consumption and service-level agreements (SLAs) violation are the challenging issues considering that the demand for computational power is growing rapidly, thereby ...requiring large-scale cloud data centers. Although, there are many existing energy-aware approaches focusing on minimizing energy consumption while ignoring the SLA violation at the time of a virtual machine (VM) selection from overloaded hosts. Also, they do not consider that the current network traffic causes performance degradation and thus may not really reduce SLA violation under a variety of workloads. In this context, this paper proposes three adaptive models, namely, gradient descent-based regression (Gdr), maximize correlation percentage (MCP), and bandwidth-aware selection policy (Bw), that can significantly minimize energy consumption and SLA violation. Energy-aware methods for overloaded host detection and VM selection from an overloaded host are necessary to improve the energy efficiency and SLA violation of a cloud data center after migrating all VM from underloaded host turn to idle host, which switch to energy-saving mode is also beneficial. Gdr and MCP are adaptive energy-aware algorithms based on the robust regression model, for overloaded host detection. A Bw dynamic VM selection policy selects VM according to the network traffic from the overloaded host under SLAs. Experimental results on the real workload traces show that the proposed algorithms reduce energy consumption while maintaining the required performance levels in a cloud data center using a CloudSim simulator to validate the proposed algorithms.
Restoring immune balance by targeting macrophage polarization is a potentially valuable therapeutic strategy for ulcerative colitis (UC). Dioscin is a steroidal saponin with potent anti-inflammatory, ...immunoregulatory, and hypolipidemic effects. This study examined the protective effect of Dioscin on UC in mice and explored the underlying mechanisms. Mice were induced colitis by dextran sulfate sodium (DSS) and concurrently treated with Dioscin oral administration. RAW264.7 cells were skewed to M1 macrophage polarization by lipopolysaccharide (LPS) and interferon-γ (INF-γ) in vitro, and received Dioscin treatment. The results showed that Dioscin ameliorated colitis in mice, reduced macrophage M1 polarization, but markedly promoted M2 polarization in mice colon. Dioscin inhibited mammalian target rapamycin complex 1 (mTORC1)/hypoxia-inducible factor-1α (HIF-1α) signaling and restrained glycolysis in RAW264.7; however, it activated mammalian target rapamycin complex 2 (mTORC2)/peroxisome proliferator-activated receptor-γ (PPAR-γ) signal and facilitated fatty acid oxidation (FAO). The modulation of mTORs signaling may inhibit M1, but promote M2 polarization. Furthermore, the effect of Dioscin on M2 polarization was neutralized by the FAO inhibitor Etomoxir and the mTORC2 inhibitor JR-AB2-011. In parallel, the inhibitory effect of Dioscin on M1 polarization was mitigated by the mTORC1 agonist L-leucine. Both JR-AB2-011 and L-leucine blocked the therapeutic effect of Dioscin in mice with UC. Therefore, Dioscin ameliorated UC in mice, possibly by restraining M1, while skewing M2 polarization of macrophages. Regulation of mTORC1/HIF-1α and mTORC2/PPAR-γ signals is a possible mechanism by which Dioscin inhibited aerobic glycolysis and promoted FAO of macrophages. In summary, Dioscin protected mice against DSS-induced UC by regulating mTOR signaling, thereby adjusting macrophage metabolism and polarization.
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The use of virtualization technology in industrial control is increasing. However, virtual instances or virtual machines (VMs) are confronted with the unreasonable resource allocation in the ...industrial control cyber range, thereby highlighting the increasing importance of resource scheduling. In the Xen open source system, the input-output (IO)-intensive task response is extended because the system does not distinguish between CPU- and IO-intensive tasks. Therefore, this article presents an improved task performance through the hybrid scheduling of CPU and network IO resources. This method uses Cap- and Timeslice-scheduling algorithms for CPU resource scheduling. First, the Cap-scheduling algorithm uses historical data to train a recurrent neural network model for classification and then utilizes the heuristic method to set the upper limit of cap value for VMs. Second, the Timeslice-scheduling algorithm adjusts the timeslice based on Q-learning to shorten the execution time of the overall tasks. This article proposes an IOb-scheduling algorithm for network bandwidth scheduling. The part that does not exceed the average bandwidth is eliminated and distributed to other VMs by monitoring the bandwidth usage of each VM, thereby improving the utilization of bandwidth. Experimental results showed that the proposed CPU/IO scheduling algorithms improved the overall benchmark performance substantially.
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•Palmatine alleviated DSS-induced experimental colitis in mice.•Palmatine suppressed NLRP3 inflammasomes activation in DSS-induce mice and macrophages.•Palmatine suppressed NLRP3 ...inflammasomes activation through promoting PINK1/Parkin-mediated mitophagy in DSS-induce mice and macrophages.
Activation of NLRP3 inflammasomes is crucial in the pathological process of Ulcerative colitis (UC), which could be negatively regulated by PINK1/Parkin-driven mitophagy. Palmatine is a herb derived isoquinoline alkaloid with potent anti-inflammatory and anti-bacteria activities. In present study, we evaluated the effect of palmatine on dextran sulfate sodium (DSS)-induced mice colitis and examined whether its effect is exerted by promoting mitophagy-mediated NLRP3 inflammasome inactivation. The result showed that palmatine (40, 100 mg/kg) significantly prevented bodyweight loss and colonic shortening in DSS mice, and reduced the disease activity index and histopathologic score. The levels of MPO, IL-1β, TNF-α and the number of F4/80+ cells in colon of DSS mice were remarkably decreased by palmatine. Moreover, palmatine suppressed NLRP3 inflammasomes activation, but enhanced the expression of the mitophagy-related proteins involving LC3, PINK1 and Parkin in colonic tissue of DSS mice. These effects was consistent with the in vitro data revealing that palmatine inhibited the activation of NLRP3 inflammasomes, while promoted the expression and mitochondrial recruitment of PINK1 and Parkin in THP-1 cell differentiated macrophages. Furthermore, the effect of palmatine on THP-1 cells was neutralized by a mitophagy inhibitor Cyclosporin A (CsA) and PINK1-siRNA. In parallel, CsA significantly attenuated the therapeutic effect of palmatine in DSS mice, illustrating that the anti-colitis effect of palmatine is closely related to mitophagy. Taken together, the current results demonstrated that palmatine protected mice against DSS-induced colitis by facilitating PINK1/Parkin-driven mitophagy and thus inactivating NLRP3 inflammasomes in macrophage.
Data-driven industrial manufacturing services are proliferating. They use large amounts of data generated from Industrial-Internet-of-Things (IIoT) devices for intelligent services to ...end-service-users. However, cloud data centers hosting these services consume a huge amount of energy, resulting in a high operational cost. To address this issue, an energy-efficient resource allocation framework is proposed in this article for cloud services. It operates in two phases. First, a multithreshold-based host CPU utilization classification scheme is developed to classify hosts into four groups for improved CPU resource allocation. It is designed through analyzing CPU utilization data by using the least median squares regression technique. Thereby, the scheme limits search space, thus reducing time complexity. In the second phase, with a metaheuristic search, an energy- and thermal-aware resource allocation method is developed to find an energy-efficient host for allocating resources to services. From real data center workload traces, extensive experiments show that our framework outperforms existing baseline approaches with 6.9%, 33.75%, and 34.1% on average in terms of temperature, energy consumption, and service-level-agreement violation, respectively.
A better understanding of the spatial pattern of soil organic matter (SOM) is important for scientific soil management. As multisource secondary data become increasingly cheap and readily available, ...numerous methods have been established to incorporate secondary information; however, these methods exhibit limitations under certain conditions due to their relatively strict requirements on secondary data. In this study, we tried to integrate sampled soil data and secondary data more effectively within the framework of Bayesian maximum entropy (BME). Specifically, multiple linear regression (MLR) and geographically weighted regression (GWR) were run 100 times based on environmental covariates such as terrain indices, vegetation indices and categorical variables obtained from soil maps. Then, the 95% confidence interval was derived from the multiple prediction values at each of the soft data points. For comparison, some conventional techniques, including ordinary kriging (OK), regression kriging (RK) and geographically weighted regression kriging (GWRK), were also applied. The results showed that BME exhibited a prediction accuracy comparable to that of OK and maintained the prediction uncertainty at a low level, while other studied methods (MLR, GWR, RK and GWRK) were all significantly inferior to BME and OK. The proposed methodology in this study represents a promising scenario for the digital soil mapping, especially when the relationships between the target soil attributes and various secondary information are not strong or residuals of trend models show insignificant spatial autocorrelation.
•Prediction confidence intervals of regression models can be used as soft data.•Incorporation of localized soft data optimizes estimation accuracy and uncertainty.•Bayesian maximum entropy with localized soft data is feasible in soil modeling.
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