We propose the cross-layer based opportunistic multi-channel medium access control (MAC) protocols, which integrate the spectrum sensing at physical (PHY) layer with the packet scheduling at MAC ...layer, for the wireless ad hoc networks. Specifically, the MAC protocols enable the secondary users to identify and utilize the leftover frequency spectrum in a way that constrains the level of interference to the primary users. In our proposed protocols, each secondary user is equipped with two transceivers. One transceiver is tuned to the dedicated control channel, while the other is designed specifically as a cognitive radio that can periodically sense and dynamically use the identified un-used channels. To obtain the channel state accurately, we propose two collaborative channel spectrum-sensing policies, namely, the random sensing policy and the negotiation-based sensing policy, to help the MAC protocols detect the availability of leftover channels. Under the random sensing policy, each secondary user just randomly selects one of the channels for sensing. On the other hand, under the negotiation-based sensing policy, different secondary users attempt to select the distinct channels to sense by overhearing the control packets over the control channel. We develop the Markov chain model and the M/G Y /1-based queueing model to characterize the performance of our proposed multi-channel MAC protocols under the two types of channel-sensing policies for the saturation network and the non-saturation network scenarios, respectively. In the non-saturation network case, we quantitatively identify the tradeoff between the aggregate traffic throughput and the packet transmission delay, which can provide the insightful guidelines to improve the delay-QoS provisionings over cognitive radio wireless networks.
In next-generation network architecture, the Cybertwin drove the sixth generation of cellular networks sixth-generation (6G) to play an active role in many applications, such as healthcare and ...computer vision. Although the previous sixth-generation (5G) network provides the concept of edge cloud and core cloud, the internal communication mechanism has not been explained with a specific application. This article introduces a possible Cybertwin based multimodal network (beyond 5G) for electrocardiogram (ECG) patterns monitoring during daily activity. This network paradigm consists of a cloud-centric network and several Cybertwin communication ends. The Cybertwin nodes combine support locator/identifier identification, data caching, behavior logger, and communications assistant in the edge cloud. The application focuses on monitoring the ECG patterns during daily activity because few studies analyze them under different motions. We present a novel deep convolutional neural network based human activity recognition classifier to enhance identification accuracy. The healthcare monitoring values and potential clinical medicine are provided by the Cybertwin based network for ECG patterns observing.
Making the best use of the dedicated short range communications multichannel architecture, we propose a cluster-based multichannel communications scheme that can support not only public-safety ...message delivery but also a wide range of future multimedia (e.g., video/audio) and data (e.g., e-maps, road/vehicle traffic/weather information) applications. Our proposed scheme integrates clustering with contention-free and/or -based medium access control (MAC) protocols. In our scheme, the elected cluster-head vehicle functions as the coordinator to collect/deliver real-time safety messages within its own cluster and forward the consolidated safety messages to the neighboring cluster heads. In addition, the cluster-head vehicle controls channel assignments for cluster-member vehicles transmitting/receiving nonreal-time traffics, which makes the wireless channels more efficiently utilized for vehicle-to-vehicle (V2V) nonreal-time data transmissions. Our scheme uses the contention-free MAC within a cluster and the contention-based IEEE 802.11 MAC among cluster-head vehicles such that the real-time delivery of safety messages can be guaranteed. Under our proposed scheme, we develop an analytical model to study the delay for the consolidated safety messages transmitted by the cluster-head vehicles. Based on this analytical model, we derive the desirable contention-window size, which can best balance the tradeoff between the delay of safety messages and the successful rate of delivering safety messages. The extensive simulation results show that, under various highway traffic scenarios, our proposed scheme can efficiently support the nonreal-time traffics while guaranteeing the real-time delivery of the safety messages.
Aspect-based sentiment analysis is a fine-grained sentiment analysis task, which needs to detection the sentiment polarity towards a given aspect. Recently, graph neural models over the dependency ...tree are widely applied for aspect-based sentiment analysis. Most existing works, however, they generally focus on learning the dependency information from contextual words to aspect words based on the dependency tree of the sentence, which lacks the exploitation of contextual affective knowledge with regard to the specific aspect. In this paper, we propose a graph convolutional network based on SenticNet to leverage the affective dependencies of the sentence according to the specific aspect, called Sentic GCN. To be specific, we explore a novel solution to construct the graph neural networks via integrating the affective knowledge from SenticNet to enhance the dependency graphs of sentences. Based on it, both the dependencies of contextual words and aspect words and the affective information between opinion words and the aspect are considered by the novel affective enhanced graph model. Experimental results on multiple public benchmark datasets illustrate that our proposed model can beat state-of-the-art methods.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
COVID-19 suddenly struck Wuhan at the end of 2019 and soon spread to the whole country and the rest of world in 2020. To mitigate the pandemic, China authority has taken unprecedentedly strict ...measures across the country. That provides a precious window to study how the air quality response to quick decline of anthropogenic emissions in terms of national scale, which would be critical basis to make atmospheric governance policies in the future. In this work, we utilized observations from both remote sensing and in-situ measurements to investigate impacts of COVID-19 lockdown on different air pollutions in different regions of China. It is witnessed that the PM2.5 concentrations exhibited distinct trends in different regions, despite of plunges of NO2 concentrations over the whole country. The steady HCHO concentration in urban area provides sufficient fuels for generations of tropospheric O3, leading to high concentrations of O3, especially when there is not enough NO to consume O3 via the titration effect. Moreover, the SO2 concentration kept steady at a low level regardless of cities. As a conclusion, the COVID-19 lockdown indeed helped reduce NO2 concentration. However, the atmospheric quality in urban areas of China has not improved overall due to lockdown measures. It underscores the significance of comprehensive control of atmospheric pollutants in cleaning air. Reducing VOCs (volatile organic compounds) concentrations in urban areas would be a critical mission for better air quality in the future.
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•NO2 plunged across China while PM2.5 kept steady or even increased as a response to COVID-19 lockdown.•HCHO, as a proxy to VOCs, kept steady in major cities though decreased in other regions during the lockdown period.•The contribution of meteorological factors to changes in the atmospheric environment is secondary.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
We propose the cross-layer based battery-aware time division multiple access (TDMA) medium access control (MAC) protocols for wireless body-area monitoring networks in wireless healthcare ...applications. By taking into account the joint effect of electrochemical properties of the battery, time-varying wireless fading channels, and packet queuing characteristics, our proposed schemes are designed to prolong the battery lifespan of the wireless sensor nodes while guaranteeing the reliable and timely message delivery, which is critically important for the patient monitoring networks. In addition, we develop a Markov chain model to analyze the performance of our proposed schemes. Both the obtained analytical and simulation results show that our proposed schemes can significantly increase the battery lifespan of sensor nodes while satisfying the reliability and delay-bound quality of service (QoS) requirements for wireless body-area monitoring networks. Furthermore, the case study of the electrocardiogram (ECG) monitoring application shows that besides meeting the delay requirements, our proposed schemes outperform the IEEE 802.15.4 and Bluetooth protocols in terms of battery lifespan.
Atmospheric aerosols and fine particulate matter (PM
) are strongly affecting human health and climate in the Anthropocene, that is, in the current era of globally pervasive and rapidly increasing ...human influence on planet Earth. Poor air quality associated with high aerosol concentrations is among the leading health risks worldwide, causing millions of attributable excess deaths and years of life lost every year. Besides their health impact, aerosols are also influencing climate through interactions with clouds and solar radiation with an estimated negative total effective radiative forcing that may compensate about half of the positive radiative forcing of carbon dioxide but exhibits a much larger uncertainty. Heterogeneous and multiphase chemical reactions on the surface and in the bulk of solid, semisolid, and liquid aerosol particles have been recognized to influence aerosol formation and transformation and thus their environmental effects. However, atmospheric multiphase chemistry is not well understood because of its intrinsic complexity of dealing with the matter in multiple phases and the difficulties of distinguishing its effect from that of gas phase reactions.Recently, research on atmospheric multiphase chemistry received a boost from the growing interest in understanding severe haze formation of very high PM
concentrations in polluted megacities and densely populated regions. State-of-the-art models suggest that the gas phase reactions, however, are not capturing the high concentrations and rapid increase of PM
observed during haze events, suggesting a gap in our understanding of the chemical mechanisms of aerosol formation. These haze events are characterized by high concentrations of aerosol particles and high humidity, especially favoring multiphase chemistry. In this Account, we review recent advances that we have made, as well as current challenges and future perspectives for research on multiphase chemical processes involved in atmospheric aerosol formation and transformation. We focus on the following questions: what are the key reaction pathways leading to aerosol formation under polluted conditions, what is the relative importance of multiphase chemistry versus gas-phase chemistry, and what are the implications for the development of efficient and reliable air quality control strategies? In particular, we discuss advances and challenges related to different chemical regimes of sulfate, nitrate, and secondary organic aerosols (SOAs) under haze conditions, and we synthesize new insights into the influence of aerosol water content, aerosol pH, phase state, and nanoparticle size effects. Overall, there is increasing evidence that multiphase chemistry plays an important role in aerosol formation during haze events. In contrast to the gas phase photochemical reactions, which are self-buffered against heavy pollution, multiphase reactions have a positive feedback mechanism, where higher particle matter levels accelerate multiphase production, which further increases the aerosol concentration resulting in a series of record-breaking pollution events. We discuss perspectives to fill the gap of the current understanding of atmospheric multiphase reactions that involve multiple physical and chemical processes from bulk to nanoscale and from regional to global scales. A synthetic approach combining laboratory experiments, field measurements, instrument development, and model simulations is suggested as a roadmap to advance future research.
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IJS, KILJ, NUK, PNG, UL, UM
Inflammation is one of major contributors of diabetic osteoporosis. Adipose derived mesenchymal stem cells (AD-MSCs) show great potential to inhibit inflammation. We investigated the ...anti-osteoporosis role of AD-MSCs-derived exosomes in diabetic osteoporosis and the underlying molecular mechanism. Cellular and animal diabetic osteoporosis models were created through high glucose exposure and streptozotocin injection. AD-MSCs-derived exosomes were isolated and characterized. Pro-inflammatory cytokines and osteoclast markers were determined by ELISA. Bone mineral content and density were detected to evaluate bone loss. qRT-PCR and Western blots were performed to detect the expression of target genes. AD-MSCs-derived exosomes inhibited the secretion of IL-1β and IL-18 in HG treated osteoclasts and restored the bone loss in streptozotocin-induced diabetic osteoporosis rats. Mechanistically, AD-MSCs-derived exosomes suppress NLRP3 inflammasome activation in osteoclasts, and then reduce bone resorption and recover bone loss. AD-MSCs-derived exosomes alleviate diabetic osteoporosis through suppressing NLRP3 inflammasome activation in osteoclasts, which might be a potential cell-free therapeutic approach for diabetes-induced bone loss treatment.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
A longstanding question in computer vision concerns the representation of 3D shapes for recognition: should 3D shapes be represented with descriptors operating on their native 3D formats, such as ...voxel grid or polygon mesh, or can they be effectively represented with view-based descriptors? We address this question in the context of learning to recognize 3D shapes from a collection of their rendered views on 2D images. We first present a standard CNN architecture trained to recognize the shapes' rendered views independently of each other, and show that a 3D shape can be recognized even from a single view at an accuracy far higher than using state-of-the-art 3D shape descriptors. Recognition rates further increase when multiple views of the shapes are provided. In addition, we present a novel CNN architecture that combines information from multiple views of a 3D shape into a single and compact shape descriptor offering even better recognition performance. The same architecture can be applied to accurately recognize human hand-drawn sketches of shapes. We conclude that a collection of 2D views can be highly informative for 3D shape recognition and is amenable to emerging CNN architectures and their derivatives.
Because of time and space restrictions and the limited interaction capabilities of robots, it is preferable for teachers to construct a learning environment using digital reality in conjunction with ...robots. Doing so enables students to learn and interact in any scenario relevant to textbook material that can be effectively digitalized, while also promoting human-robot interaction. Here, an interactive situated learning approach was developed to improve students' learning performances. The students and robot role-played characters and immersed themselves in digital situated learning tasks and challenges. This approach included a real-time feedback mechanism to guide and evaluate the knowledge application of the students. The evaluation was performed during interactions with the robot, virtual objects, and virtual characters based on textbook context and content. The experiment was conducted during an English as a second language course for junior high school students. A total of 101 students were assigned to three groups with different approaches and their learning performance was evaluated. The experimental results indicated that students who learned with the proposed approach exhibited better learning achievement and significant positive effects in terms of learning motivation and engagement. Furthermore, interaction with physical robots improved student learning achievements significantly compared with virtual interaction. Moreover, motivation in the learning process could be enhanced using authentic objects and scenarios in the digital situated learning environment.
•Students were immersed in an interactive situated learning environment with a robot.•The robot provided challenges and immediate feedback to guide and evaluate students.•A situated interactive learning approach gave achievement, motivation, and engagement.•Learning achievement is enhanced by physical embodiment and interaction with robots.•Potential for digital situational learning improving students' learning motivation.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
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