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This paper investigates a multi-node wireless powered communication network (WPCN), where a hybrid access point (HAP) first charges an Internet of Thing (IoT) device wirelessly with the assistance of multiple selfish wireless nodes (WNs), and then the IoT device uses the harvested energy to transmit real-time status updates to the HAP. Two incentive schemes, i.e., the energy-incentive scheme and the price-incentive scheme, are designed to overcome the selfishness of the WNs and enhance the per-packet AoI performance. For the energy-incentive scheme, an AoI-energy utility function is defined and an optimization problem is formulated to maximize the AoI-energy utility value of the HAP-IoT device pair. By using equality constraint elimination and Lagrangian method, the problem is solved and some closed-form solutions are derived to obtain the optimal solution. For the price-incentive scheme, an AoI-price utility function is defined and a Stackelberg game is established to maximize the utility of the HAP-IoT device pair. By using function transformation and Lagrange method, some semi-closed-form solutions are derived to maximize their own profits of the HAP and WNs in a distributed way. Numerical results show that our proposed two incentive mechanisms are able to achieve higher network utility values than the benchmark scheme. The more the number of WNs, the lower the AoI of each status update packet and the higher utility value of the HAP. It also shows that by positioning WNs closer to the IoT device, the better per-packet AoI performance can be achieved by both incentive mechanisms. Additionally, for the energy-incentive mechanism, its achieved AoI gain and energy gain decrease with the increment of the distance between the HAP and the IoT device. But for the price-incentive mechanism, the opposite phenomenon is observed.

Recently, air quality issues have attracted much more attention. This paper aims to find an effective way to analyse the buildings’ effects on the air quality variation between indoor and outdoor. To do so, we treat the building as a dynamic filter system by regarding the outdoor PM $_{2.5}$ , the indoor PM $_{2.5}$ , and the building as the input, the output, and a response system, respectively. To analyze the filtering effect produced by buildings, the statistical distribution of the indoor PM $_{2.5}$ per hour is studied, and the interrelationship between the indoor and the outdoor PM $_{2.5}$ is explored in time domain. Some interesting physical laws are discovered by using the collected data. First, the indoor PM $_{2.5}$ per hour follows Gaussian distribution in most cases. Second, the indoor PM $_{2.5}$ has a positive correlation with the corresponding outdoor one. Third, a linear regression model with high accuracy on analyzing the indoor PM $_{2.5}$ is presented, which indicates that the indoor PM $_{2.5}$ consists of two parts—one comes from the outdoor PM $_{2.5}$ penetrating into the building and the other comes from the indoor environment. Fourth, by applying different system identification methods, it is found that the B–J model is the best one in characterizing the memory effects of the building for both long time and short time scales. Particularly, for the long time memory effect, the indoor PM $_{2.5}$ average memory duration (AMD) is about 2 h, and the indoor PM $_{2.5}$ AMD to the outdoor PM $_{2.5}$ is about 7 h, while for the short time memory effect, the indoor PM $_{2.5}$ AMD is also about 2 h but that to the outdoor PM $_{2.5}$ is about 5 h. Additionally, the continuance of outdoor PM $_{2.5}$ has much greater effect on the indoor one than its concentration.

This article investigates a relay-assisted wireless powered communication network (WPCN), where the access point (AP) inspires the auxiliary nodes to participate together in charging the sensor, and then the sensor uses its harvested energy to send status update packets to the AP. An incentive mechanism is designed to overcome the selfishness of the auxiliary node. In order to further improve the system performance, we establish a Stackelberg game to model the efficient cooperation between the AP–sensor pair and auxiliary node. Specifically, we formulate two utility functions for the AP–sensor pair and the auxiliary node, and then formulate two maximization problems respectively. As the former problem is non-convex, we transform it into a convex problem by introducing an extra slack variable, and then by using the Lagrangian method, we obtain the optimal solution with closed-form expressions. Numerical experiments show that the larger the transmit power of the AP, the smaller the age of information (AoI) of the AP–sensor pair and the less the influence of the location of the auxiliary node on AoI. In addition, when the distance between the AP and the sensor node exceeds a certain threshold, employing the relay can achieve better AoI performance than non-relaying systems.

This paper investigates a fog computing-assisted multiuser simultaneous wireless information and power transfer network, where multiple sensors with power splitting (PS) receiver architectures receive information and harvest energy from a hybrid access point (HAP), and then process the received data by using local computing mode or fog offloading mode. For such a system, an optimization problem is formulated to minimize the sensors’ required energy while guaranteeing their required information transmissions and processing rates by jointly optimizing the multiuser scheduling, the time assignment, the sensors’ transmit powers, and the PS ratios. Since, the problem is a mixed integer programming problem and cannot be solved with existing solution methods, we solve it by applying problem decomposition, variable substitutions, and theoretical analysis. For a scheduled sensor, the closed-form and semi-closed-form solutions to achieve its minimal required energy are derived, and then an efficient multiuser scheduling scheme is presented, which can achieve the suboptimal user scheduling with low computational complexity. Numerical results demonstrate our obtained theoretical results, which show that for each sensor, when it is located close to the HAP or the fog server, the fog offloading mode is the better choice; otherwise, the local computing mode should be selected. The system performances in a frame-by-frame manner are also simulated, which show that using the energy stored in the batteries and that harvested from the signals transmitted by previous scheduled sensors can further decrease the total required energy of the sensors.

This paper explores the age-energy region in a wireless powered communication network (WPCN), where an Internet of things (IoT) device works in a “harvest-then-transmit” protocol, in which the IoT device first harvests energy from radio-frequency (RF) signals transmitted by a hybrid access point (HAP) via wireless power transfer (WPT), and then transmits real-time update using the harvested energy to the HAP. For such a system, achievable age-energy region and age-energy function are defined as metrics to measure the trade-off between the age of information (AoI) and the storable energy at the IoT device. Moreover, the optimal transmission policy is designed by solving an AoI minimization problem formulated in terms of the storable energy at the IoT device, which also characterizes the boundaries of the system's age-energy region. As the problem is non-convex, it is first transformed into equivalent ones, and then solved with the closed-form solution. Numerical results show that the transmit power at the HAP and the distance between the HAP and the IoT device have greater effects on the storable energy than the system AoI, and the data size of the update packet has a greater effect on the system AoI than the storable energy. The minimal signal-to-noise-ratio (SNR) threshold has a bilateral influence on the age-energy region, while the impact of the maximal achievable transmit power of the IoT device on the age-energy region is not obvious.

Angiogenesis is a prerequisite for multiple myeloma development. Tumor cells can stimulate angiogenesis by secreting vascular endothelial growth factor A (VEGFA), but we previously reported that tumor angiogenesis was not significantly reduced when VEGFA expression was inhibited in myeloma cells. Tumor-associated macrophages (TAMs) are important components of the tumor microenvironment and have been reported to be involved in the regulation of angiogenesis. In this study, we performed in vitro macrophage coculture studies and studies with RPMI 8226 and TAMs cell-conditioned media to explore their effects on the proliferation, migration, and tube formation of human umbilical vein endothelial cells (HUVECs). Our results showed that M2 macrophages and RPMI 8226 cells could synergistically promote HUVEC proliferation, migration, and tube formation, and that VEGFA depletion in both cell types suppressed HUVEC tube formation ability. Conversely, M1 macrophages inhibited the tube formation in HUVECs. Mechanistically, M2 macrophage secretion of VEGFA may affect vascular endothelial growth factor receptor 1 signaling to regulate angiogenesis. In summary, our results suggest that macrophage clearance or inducing of transformation of M2 macrophages into M1 macrophages are potential treatment strategies for multiple myeloma.

This paper investigates the optimal design of wireless powered fog computing networks, where a hybrid access point integrated with fog computing function (F-HAP) first charges an Internet of Things (IoT) device via wireless power transfer (WPT), and then the IoT device uses the harvested energy to compute real-time updates locally or offload the updates to the F-HAP for computing. For such a system, an age-energy aware cost function is defined to evaluate the system performance, based on which, an optimization problem is formulated to explore the minimum age-energy aware cost by jointly optimizing the time assignment, the transmit power, the computing frequency, as well as the computing mode selection, such that the given data processing task can be completed. Since the problem is non-convex with the discrete binary variable, variable substitution and Karush-Kuhn-Tucker (KKT) conditions are applied to solve it and some closed-form results on the optimal solution are derived. Numerical results show that the transmit power has much greater effects on the age-energy performance of the fog offloading mode than on that of the local computing mode. Moreover, when the IoT device is relatively close to the F-HAP, fog offloading is a better choice; Otherwise, local computing should be selected.

Recently, people are paying increasing attention to the air quality issues. In order to explore the effects of buildings on the change from the outdoor PM 2.5 to indoor ones, an effective model-fitting approach based on known knowledge is proposed to analyze the collected data from signals and systems perspective. With our presented approach, some interesting physical laws are discovered. It is found that the building produces a filtering effect on the outdoor PM 2.5 . The statistical distribution of indoor PM 2.5 per hour follows Gaussian distribution in most cases and the indoor PM 2.5 has a positive correlation with the outdoor PM 2.5 . Moreover, the indoor PM 2 .g consists of two parts: one is from the outdoor PM 2.5 penetrating into the building, and the other is from the indoor background PM 2.5 . It is also shown that the B-J model is the best mode in characterizing the memory effects of the building both for long-time scale and short-time scale analysis. For the long-time scale memory effect, the memory of current indoor PM 2 .5 to the historical indoor PM 2 .g is about 2 hours and that to the outdoor PM 2.5 is about 7 hours, while for the short-time scale memory effect, the memory of current indoor PM 2.5 to the historical indoor PM 2.5 is about 2 hours and that to the outdoor PM 2 .5 is about 5–8 hours. This paper also indicates that the model-fitting approach from signals and systems perspective provides a very effective way to analyse PM 2.5 data.

This paper studies a simultaneous wireless information and power transfer (SWIPT)-aware fog computing by using a simple model, where a sensor harvests energy and receives information from a hybrid access point (HAP) through power splitting (PS) receiver architecture. Two information processing modes, local computing and fog offloading modes are investigated. For such a system, two optimization problems are formulated to minimize the sensor&rsquo, s required power for the two modes under the information rate and energy harvesting constraints by jointly optimizing the time assignment and the transmit power, as well as the PS ratio. The closed-form and semi-closed-form solutions to the proposed optimization problems are derived based on convex optimization theory. Simulation results show that neither mode is always superior to the other one. It also shows that when the number of logic operations per bit associated with local computing is less than a certain value, the local computing mode is a better choice, otherwise, the fog offloading mode should be selected. In addition, the mode selection associated with the positions of the user for fixed HAP and fog server (FS) is also discussed.

OBJECTIVE To develop a HPLC method for the determination of plasma concentration of oridonin (ORI) and study the pharmacokinetics of ORI in mice. METHOD Blood was sampled from mice which were injected ORI by 10 mg x kg(-1) at different time intervals, and the concentration of ORI was determined by HPLC. The pharmacokinetic parameters were accessed by 3P97. RESULT The calibration curve was linear (r = 0.998 7) within the range of 0.202-20.0 mg x L(-1) for ORI in plasma. The average recoveries were more than 93%. The within-day and between-day precisions were no more than 9%. After i.v. oridonin in mice, the plasma concentration-time course fitted well to two-compartment model. The pharmacokinetic equation was C = 16.192 5e(-0.554 6t) + 5.475 7e(-0.016 3t). The pharmacokinetic parameters were below: t1/2alpha 1.249 9 min, t1/2beta 42.638 4 min, K21 0.152 3 min(-1), K12 0.359 3 min(-1), K10 0.0592 min(-1), AUC 366.035 0 microg x min x mL(-1), CL 0.0273 L x min(-1) x kg(-1), V(c)0.461 5 L x kg(-1). CONCLUSION The method can be used to determine the concentration and to investigate the pharmacokinetics of ORI in mice. ORI was absorbed and distributed very fast in mice. The effect of ORI was rapid. The elimination was the main process.