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Maintaining hand hygiene has been an essential preventive measure for reducing disease transmission in public facilities, particularly during the COVID-19 pandemic. The large number of sanitizer stations deployed within public facilities, such as on university campuses, brings challenges for effective facility management. This paper proposes an IoT sensor network for tracking sanitizer usage in public facilities and supporting facility management using a data-driven approach. Specifically, the system integrates low-cost wireless sensors, LoRaWAN, and cloud-based computing techniques to realize data capture, communication, and analysis. The proposed approach was validated through field experiments in a large building on a university campus to assess the network signal coverage and effectiveness of sensor operation for facility monitoring. The results show that a LoRaWAN created from a single gateway can successfully connect to sensors distributed throughout the entire building, with the sensor nodes recording and transmitting events across the network for further analysis. Overall, this paper demonstrates the potential of leveraging the IoT-based Sanitizer Station Network to track public health mitigation methods in a large facility, which ultimately contributes to reducing the burden of maintaining public health during and post-pandemic.

Network Calculus is an established analytical framework which can provide deterministic estimates of latency and buffer requirements for feed-forward packet-switched communication networks. Researchers find it useful in analyzing network performance in real-time systems. As there are several Network Calculus tools and software packages for estimating network performance, it is important to select suitable Network Calculus tools for network analysis problems. This article introduces twelve established tools for Network Calculus analysis and compares their advantages and disadvantages in terms of their applicability for various systems and analysis needs. We also provide recommendations for users to select the suitable tools for research. Five public tools, namely DiscoDNC, RTC Toolbox, Deborah, CyNC, and nc-tandem-tight, are chosen to analyze end-to-end delay in a series of tandem networks for their performance demonstration. Simulations of the same tandem networks are implemented to compare with the analytical results achieved by the Network Calculus tools. To our best knowledge, this is the first comprehensive study that compares existing tools of Network Calculus analysis for real-time system network infrastructure. Our aim is to provide quick guidance to research communities for selecting suitable tools to analyze delay performance of networks.

The value iteration mechanism is introduced to design the constrained optimal tracking controller for continuous-time nonlinear system in this paper. Unlike most studies were developed by policy iteration, the proposed VI algorithm is designed to be started with arbitrary semi-definite positive function, and thus no restriction is in the initial condition. The iterative property of the proposed algorithm is discussed and the optimality of the iterated results are obtained. To verify the effectiveness, a nonlinear case is simulated.

Though radiofrequency ablation (RFA) is considered to be an effective treatment for hepatocellular carcinoma (HCC), but more than 30% of patients may suffer insufficient RFA (IRFA), which can promote more aggressive of the residual tumor. One possible method to counter this is to accurately identify the margin of the HCC. Colony-stimulating factor 1 receptor (CSF-1R) has been found to be restrictively expressed by tumor associated macrophages (TAMs) and monocytes which more prefer to locate at the boundary of HCC. Using biotinylation method, we developed a CSF-1R-conjugated nanobubble CSF-1R (NBCSF-1R) using a thin-film hydration method for margin detection of HCC. CSF-1R expression was higher in macrophages than in HCC cell lines. Furthermore, immunofluorescence showed that CSF-1R were largely located in the margin of xenograft tumor and IFRA models. In vitro, NBCSF-1R was stable and provided a clear ultrasound image even after being stored for 6 months. In co-culture, NBCSF-1R adhered to macrophages significantly better than HCC cells (p = 0.05). In in vivo contrast-enhanced ultrasound imaging, the washout half-time of the NBCSF-1R was significantly greater than that of NBCTRL and Sonovue® (p = 0.05). The signal intensity of the tumor periphery was higher than the tumor center or non-tumor region after NBCSF-1R injection. Taken together, NBCSF-1R may potentially be used as a non-invasive diagnostic modality in the margin detection of HCC, thereby improving the efficiency of RFA. This platform may also serve as a complement method to detect residual HCC after RFA; and may also be used for targeted delivery of therapeutic drugs or genes.

The electrochemical reduction of water to produce molecular hydrogen is a potential strategy for generating clean, renewable energy. However, the development of an efficient and durable catalyst remains a significant challenge. Inspired by nature, we herein describe a three-dimensional, porous hybrid catalyst fabricated by using a biomimetic approach, in which a small amount of ruthenium (Ru) nanoparticles is uniformly dispersed onto a carbonized collagen scaffold. Coordination between Ru nanoparticles and the active sites of collagen promotes the electrochemical formation of molecular hydrogen by altering the electronic state of Ru and regulating the free energy of hydrogen chemisorption. The prepared catalyst exhibits superior activity for hydrogen evolution, with an overpotential and Tafel slope comparable to those of a Pt/C catalyst. This work provides a novel perspective on the fabrication of high-efficiency electrochemical catalysts.

Finding the optimal parameters in laser processing applications can be time-consuming given the large parameter space and various sources of error. This problem is exacerbated by day-to-day variation in laser beam characteristics and a large variety of materials that need to be processed. The ideal laser processing system should be “smart,” meaning that it can sense changes in the environment, make proper adjustment, and predict parameters for new materials. As a step toward this goal, we propose a method to efficiently extract the areas of a large number of laser-induced damages in situ using an automated data acquisition system that can control laser parameters, motorized stage movement, image capturing/processing, and feature extraction. The damage areas are extracted and compared with direct measurements. Damage areas are fed into an artificial neural network (ANN) for prediction. Various ANN structures and training functions are tested to create the optimal ANN for prediction. ANN predictions were found to be capable enough to accurately model and optimize the laser processing parameters that were investigated. With the capability of collecting a large amount of usable data in a short period of time, this acquisition system can be used to train sophisticated ANNs for complicated tasks such as quality control and failure prediction.

The intrusion response system is dedicated to automatically respond to sophisticated network intrusions, which is a sequential decision-making problem for autonomous agents. The current Markov decision process (MDP) or stochastic games based solutions suffer from several weaknesses: (i) The MDP- based approach is unable to explicitly model the opponents; (ii) The Nash equilibrium approach of stochastic games cannot handle the condition with multi equilibria. Existing studies have not considered the cognitive ability of the agent and lack of explicit opponent modeling. Inspired by recursive reasoning, this paper introduces a theory of mind (ToM)-based stochastic game-theoretic approach to reason about the beliefs and behaviors of the attackers. Each agent maintains different order ToM beliefs concerning his opponent with explicit opponent modeling. In order to accurately predict the attacker's action with nested beliefs, we utilize the Bayesian attack graph (BAG) to model multi-step attacks scenarios. In addition, the agent is allowed to learn from new information to adjust his beliefs and learning speed. Simulation results validate that ToM modeling performs well in the intrusion response system than random defense actions. Besides, a defender with first-order ToM beliefs always wins an attacker with zero-order ToM beliefs.

The green development theory proposed by the Organization for Economic Cooperation and Development (OECD) has promoted the harmonious development of the economy, society, and environment in many countries, in particular, it has provided a good option for the coordinative development of economic growth, resource utilization, and ecological protection in rural areas of developing countries. For this reason, we used the OECD model to measure green development in arid, rural areas of China, and also subjective and objective weighting methods to measure the rural green development level of 78 county-level regions in Shaanxi province in 2018. At the same time, the least square error (LSE) method was used to determine the contribution rate of government support, environmental pressure, resource endowment, and quality of life, so as to determine the influencing factors of rural green development in Shaanxi. The results show that the levels of rural green development in Shaanxi province differed internally: the level of green development in the north was strong, moderate in the southwest and northwest, and weak in the center and south. The driving types of rural green development in Shaanxi province are divided into five types: Three Factors I, Three Factors II, Four Factors I, Four Factors II, and Five Factors, the influencing factors of rural green development are varied from county to county. In terms of different regions, different development approaches and countermeasures are proposed respectively. This research provides scientific guidance for local government to formulate green agricultural development policies and to overcome the development difficulties in rural areas.

Recently, link flooding attacks (LFA) have been observed as a serious threat for cutting off the Internet connectivity through congesting critical links. A LFA typically utilizes legitimate and low-rate flows, which makes it extremely hard to be detected and, subsequently, to be mitigated. In this paper, we present LF-Shield, that is a deep convolutional neural network (ConvNet) based countermeasure to accurately detect and efficiently mitigate LFAs using software-defined network (SDN) paradigm. LF-Shield can identify malicious bots that launch LFA flows by extracting end-hosts’ traffic features and afterwards, classifying the type of end-hosts based on deep ConvNet. Then, LF-Shield mitigates LFAs without affecting legitimate end-hosts through blocking the classified malicious bots and limiting the bandwidths of inactive or newly-accessed end-hosts. A LF-Shield prototype is implemented for evaluating its performance by several experiments. The experimental results demonstrate that LF-Shield can identify malicious bots with an accuracy of 96.4% and mitigate LFAs with the 93.1% reduction in link degradation ratio, with negligible impact on legitimate end-hosts.

Despite the functions of anti-PD-1 antibodies as immune checkpoint regulators, less than 30% of patients exhibit durable therapeutic responses to anti-PD-1 antibodies. Studies have shown that insufficient infiltration of immune cells might limit the outcome of anti-PD-1 therapy. Therefore, we synthesized an immune cell-recruiting liposomal system (FN-nps) to improve this therapeutic strategy. The FN-nps could generate cell debris and expose heat shock protein 70, which could recruit immune cells to tumor sites to assist in anti-PD-1 treatment. In vivo experiments revealed that the FN-nps could assist in anti-PD-1 therapy by increasing the number of lymphocytes in the peripheral blood and tumor site by generating tumor antigens, and this effect was accompanied by an increase in cytokine expression. The number of CTLs increased and mRNA expression levels of cytokines were regulated when the FN-nps were combined with anti-PD-1 therapy. The revealed properties of the liposomal system make it highly promising for assisting in anti-PD-1 antibody immunotherapy in different cancers.

Cadmium chalcogenide colloidal quantum wells or nanoplatelets (NPLs), a class of new materials with atomically precise thickness and quantum confinement energy, have shown great potential in optoelectronic applications. Short exciton lifetimes in two-dimensional (2D) NPLs can be improved by the formation of type II heterostructures, whose properties depend critically on the mechanism of exciton transport. Herein, we report a study of room-temperature exciton in-plane transport mechanisms in type-II CdSe/CdTe core/crown (CC) colloidal NPL heterostructures with the same CdSe core and different CdTe crown sizes. Photoluminescence excitation measurements show unity quantum efficiency for transporting excitons created at the crown to the CdSe/CdTe interface (to form lower-energy charge-transfer excitons). At near band edge excitation, the crown-to-core transport time increases with crown size (from 2.7 to 5.6 ps), and this size-dependent transport can be modeled well by 2D diffusion of thermalized excitons in ...

Fault diagnosis and controller design are two important aspects to improve proton exchange membrane fuel cell (PEMFC) system durability. However, the two tasks are often separately performed. For example, many pressure and voltage controllers have been successfully built. However, these controllers are designed based on the normal operation of PEMFC. When PEMFC faces problems such as flooding or membrane drying, a controller with a specific design must be used. This paper proposes a unique scheme that simultaneously performs fault diagnosis and tolerance control for the PEMFC system. The proposed control strategy consists of a fault diagnosis, a reconfiguration mechanism and adjustable controllers. Using a back-propagation neural network, a model-based fault detection method is employed to detect the PEMFC current fault type (flooding, membrane drying or normal). According to the diagnosis results, the reconfiguration mechanism determines which backup controllers to be selected. Three nonlinear controllers based on feedback linearization approaches are respectively built to adjust the voltage and pressure difference in the case of normal, membrane drying and flooding conditions. The simulation results illustrate that the proposed fault tolerance control strategy can track the voltage and keep the pressure difference at desired levels in faulty conditions.

Pursuing ever-smaller feature size in laser-based lithography is a research topic of vital importance to keep this technique competitive with other micro-/nano-fabrication methods. Features smaller than the diffraction-limited spot size can be obtained by “thresholding”, which utilizes the deterministic nature of damage threshold with ultrashort laser pulses and is achieved by precisely tuning pulse energies so that only the central portion of the focal spot produces permanent modification. In this paper, we examine the formulation commonly used to describe thresholding and show that the relationship between feature size (r) and laser fluence (F) is invariant with respect to the nature of laser absorption. Verified by our experiments performed on metal, semiconductor, and dielectric samples, such invariance is used to predict the smallest feature size that can be achieved for different materials in a real-world system.

Two-dimensional (2D) nanomaterials have attracted much interest as promising candidates for next generation energy storage, electronics and catalytic applications, due to their unique topology and electronic structure. However, the development of an operable and versatile solution processing technique for simple fabrication of high-quality 2D nanosheets continues to present a significant challenge. Here, an ultrathin indium sulfide nanosheet is synthesized via a simple solution-phase reaction involving two steps: i) thermal decomposition of precursors to form a crystal nucleus; ii) growth of 2D nanosheets in a mixture of amino ligand and alkane solvent. The alkane, matching the amino ligand structure in terms of geometry, could assemble with the ligand to form a soft template and thus induce a 2D arrangement of the nucleus. Moreover, the chemical inertness of the alkane facilitated rotation of crystal seeds, resulting in anisotropic growth of the nanosheet. We further demonstrated that the geometrically matched alkane-assisted solution processing reaction could be applied to synthesize various 2D metal chalcogenides. Functionally, the acquired indium sulfide nanosheets, which possess high photoelectric activity, were introduced for fabrication of photoelectrodes capable of efficient photoelectrochemical water splitting. Our work opens up a new perspective toward the construction and potential applications for various 2D nanomaterials.

We demonstrate a method of laser ablation with reduced feature size by using a pair of ultrashort pulses that are partially overlapped in space. By tuning the delay between the two pulses, features within the overlapping area are obtained on the surface of fused silica. The observed dependence of the feature position on delays longer than the free-carrier lifetime indicates an ionization pathway initiated by self-trapped excitons. This method could be used to enhance the resolution of laser-based lithography.