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Abstract CD320, which is a transmembrane protein responsible for facilitating the absorption of vitamin B12, plays a key role in this process. However, the relationships between CD320 and immune cell infiltration levels remain unclear, with limited studies investigating the diagnostic and prognostic significance of CD320 in hepatocellular carcinoma. We used various databases, including the TIMER, GEPIA, UALCAN and TCGA databases to investigate the expression levels of CD320 in hepatocellular carcinoma. Subsequently, we analyzed the prognosis of hepatocellular carcinoma patients with different expression levels of CD320. Furthermore, we also performed Western blot, immunohistochemistry, and immunofluorescence analyses to validate the results of the database analysis. Finally, the functions of CD320 in hepatocellular carcinoma were also confirmed via relevant cell experiments and angiogenesis assays. We found that CD320 expression was significantly upregulated in tumor vascular endothelial cells. Moreover, the knockdown of CD320 led to a reduction in angiogenesis in endothelial cells. Increased expression of CD320 was also correlated with a poor prognosis in patients with hepatocellular carcinoma, which suggested that CD320 may be a potential prognostic marker. Finally, TIMER analysis demonstrated that the infiltration of six immune cell types was significantly associated with high expression levels of CD320 in hepatocellular carcinoma. Herein, we demonstrated that CD320 may play an important role in angiogenesis in hepatocellular carcinoma. These findings suggested that CD320 may be a potential clinical prognostic marker and immunotherapy target for hepatocellular carcinoma.

During the process of forest succession, coexisting plant species fulfill diverse functions by modifying the interrelationships among functional traits. Examining the interrelationships of functional traits across multiple dimensions would enable a comprehensive understanding of the plants’ adaptive strategies during forest succession. Plant trait networks (PTNs) comprehensively capture and visualize these intricate relationships among functional traits. Nonetheless, the changes in PTNs during forest secondary succession remain unclear. In this study, we measured 24 functional traits related to leaf resource acquisition, photosynthesis, hydraulics, and physical defense across tree species at three successional stages: young forest (YF), intermediate forest (IF), and old-growth forest (OF) in the Ailao Mountains, Southwest China. We established leaf trait networks (LTNs) to investigate the interrelationships among functional traits during forest succession. Our results revealed a decrease in the Edge Density decreased and an increase in the Modularity as succession advanced. In the YF stage, leaf nitrogen, photosynthetic rate, and force to punch were the hub traits; whereas, canopy exposure and leaf dry mass were highly connected traits in the IF and OF stages, respectively. The connectivity of highly connected traits was related to the plant’s life-history strategies and environmental adaptations at different successional stages. In terms of the importance of the four trait dimensions, resource acquisition and photosynthetic traits consistently demonstrated strong connectivity within LTNs throughout the succession. We concluded that co-occurring tree species adapted to the changing environmental conditions by adjusting trait modules through alternative strategies during secondary succession. Specifically, resource acquisition and trait coordination were more advantageous in the YF stage, while higher modularity offered greater opportunities for niche differentiation in the OF stage. This study provides new insights into exploring the mechanisms of forest succession through the analysis of trait correlation networks.

The deep learning-based change detection (CD) methods have achieved remarkable progress with remote sensing imagery. These methods mainly rely on complex feature extraction structures and numerous attention mechanisms to realize effective feature extraction and recognition. However, this results in a significant increase in the number of parameters and the training cost of the whole network. The increase in parameterization can also lead to the degradation of network performance when the amount of training data is insufficient. Thus, it is still promising and challenging to perform reliable CD results through light network design. In this article, we propose a lightweight full-scale hybrid network. The network is comprised of a convolutional neural network (CNN), multilayer perceptron (MLP), and transformer, and it is capable of achieving high performance in CD tasks with a lightweight structure. First, the MLP structures are integrated into the basic network to extract global feature information, compensating for the information loss caused by the convolutional operations of CNN. Second, a full-scale difference module is designed to sufficiently extract the feature information and ensure enough feedforward information. Third, a lightweight transformer is appended at the end of the network to accomplish the spatial-temporal correlation of features, which effectively enhances the quality of the final extracted features. Experimental results on three classical CD datasets show that the proposed method outperforms the state-of-the-art methods.

Abstract The geographical variation and domestication of tree species are an important part of the theory of forest introduction, and the tracing of the germplasm is the theoretical basis for the establishment of high‐quality plantations. Chinese pine (Pinus tabuliformis Carr.) is an important native timber tree species widely distributed in northern China, but it is unclear exactly where germplasm of the main Chinese pine plantation populations originated. Here, using two mtDNA markers, we analyzed 796 individuals representing 35 populations (matR marker), and 873 individuals representing 38 populations (nad5‐1 marker) of the major natural and artificial populations in northern China, respectively (Shanxi, Hebei and Liaoning provinces). The results confirmed that the core position of natural SX* populations (“*” means natural population) in the Chinese pine populations of northern China, the genetic diversity of HB and LN plantations was higher than that of natural SX* populations, and there was a large difference in genetic background within the groups of SX* and LN, HB showed the opposite. More importantly, we completed the “point by point” tracing of the HB and LN plantings. The results indicated that almost all HB populations originated from SX* (GDS*, ZTS*, GCS*, and THS*), which resulted in homogeneity of the genetic background of HB populations. Most of germplasm of the LN plantations originated from LN* (ZJS* and WF*), and the other part originated from GDS* (SX*), resulting in the large differences in the genetic background within the LN group. Our results provided a reliable theoretical basis for the scientific allocation, management, and utilization of Chinese pine populations in northern China, and for promoting the high‐quality establishment of Chinese pine plantations.

Abstract The 0–1 mixed integer programming problem of binary offloading on wireless powered mobile edge computing (WP‐MEC) networks requires joint optimization of binary and continuous variables, which is computationally expensive for traditional techniques and difficult to solve within the channel coherence time under time‐varying condition. Using machine learning models to output variable values is also challenging. Hence, designing efficient and low‐complexity algorithms is crucial for optimal network performance. This paper focuses on the computation latency of the FDMA‐based WP‐MEC network and proposes a task‐offloading algorithm to minimize the total completion delay (TCD). The TCD minimization is modelled as a 0–1 MIP problem and is decomposed into a master problem of optimizing the offloading decision and the sub‐problem of optimizing other parameters under a given offloading decision. The sub‐problem is solved using optimization method, while the master problem is solved using a deep reinforcement learning algorithm. Simulation results show that the proposed algorithm can achieve almost minimal TCD with low complexity.

In order to evaluate the effects of sizing agents on the wettability, strength loss, and properties of final filament wound structures during filament winding of carbon fibers, three types of sizing agent based on epoxy, epoxy + polyester, and epoxy + polyurethane were used to treat carbon fibers, the surface properties of carbon fiber samples after treatment were evaluated using SEM, infrared spectroscopy, dynamic contact angle analysis, and interlaminar shear strength (ILSS) test, the strength loss caused by fiber damage during filament winding was quantitatively analyzed, and the strength properties of the filament wound structures were characterized by NOL ring test. The results showed that the sizing agent treatment only slightly improved the surface free energy and ILSS of carbon fibers, but it had obvious influence on the strength loss rate of carbon fiber bundles. Added polyester or polyurethane in epoxy-based sizing may improve its protective effect for carbon fiber, and thus decrease strength loss during winding process, result in better tensile properties of carbon fiber filament wound structures.

This paper presents a membrane electrode assembly (MEA) with a double-catalytic layered structure to improve the performance of the micro direct methanol fuel cell. The inner and outer parts of the double-catalytic layer comprise an unsupported and carbon-supported catalyst, respectively. A two-dimensional two-phase model of mass transport and electrochemical reaction is established and simulated to analyze the superiority of the double-catalytic layered structure. Simulation results show that this structure has a more uniform current density distribution and less over-potential across the catalyst layer. Methanol crossover is also reduced. Experimental results confirm that the MEA with the double-catalytic layered structure exhibits better performance than the traditional MEA. The adoption of a gas diffusion electrode as the outer catalytic layer and a catalyst-coated membrane as the inner layer of the double-catalytic layered structure can further improve the performance of the MEA. Both simulation and experimental results show the existence of an optimum number of metal loadings of the inner and outer parts of the double-catalytic layer.

Integrated circuits suffer severe deterioration due to single-event upsets (SEUs) in irradiated environments. Spin-transfer torque magnetic random-access memory (STT-MRAM) appears to be a promising candidate for next-generation memory as it shows promising properties, such as non-volatility, speed, and unlimited endurance. One of the important merits of STT-MRAM is its radiation hardness, thanks to its core component, a magnetic tunnel junction (MTJ), being capable of good function in an irradiated environment. This property makes MRAM attractive for space and nuclear technology applications. In this paper, a novel radiation-hardened triple modular redundancy (TMR) design for anti-radiation reinforcement is proposed based on the utilization of STT-MTJ devices. Simulation results demonstrate the radiation-hardened performance of the design. This shows improvements in the design’s robustness against ionizing radiation.

Micro direct methanol fuel cells (μDMFCs) are a promising power source for microelectronic devices and systems. As the operating state and performance of a μDMFC is generally determined by both electrochemical polarization and methanol crossover, it is important to monitor the methanol concentration in μDMFCs. Here, we design and fabricate a microwave sensor and integrate it with a μDMFC for the online detection of methanol concentration in a nonintrusive way. The sensing area is set at the bottom of the anode chamber of a μDMFC which exhibits a maximum output power density of 28.8 mW cm−2 at 30 °C. With a square ring structure, the dual-mode microwave sensor shows a sensitivity of 9.5 MHz mol−1 L. Furthermore, the importance of methanol concentration monitoring is demonstrated in the long term. A relatively smooth methanol decline curve was obtained, which indicated a normal and stable operating status of the μDMFC. Derived from real-time recording data, fuel utilization was additionally calculated as 28.5%.

Abstract Mobile edge computing is the network technology for providing computing resources of edge computing server to Internet of Things (IoT) applications. Additionally, wireless power transfer (WPT) technology can provide stable energy supplying to IIoT nodes and to overcome the limited node lifetime problem faced when using batteries. In this paper, the wireless powered mobile edge computing network is considered where the edge computing server transfers RF energy to IIoT nodes which use harvested energy to offload partial computation workload based on OFDMA and also conduct local computation. The aim is to maximise the weighted sum computation rate by jointly optimising the WPT duration and the amount of energy used for offloading at each node for each time frame. This paper proposes an offloading approach based on deep reinforcement learning which is able to quickly obtain the near‐optimal offloading solutions. Specifically, the original offloading problem is decomposed into the sub‐problem of optimising the energy allocated for offloading under a given WPT duration and the top‐problem of optimising the WPT duration. Simulation results demonstrate that the proposed algorithm can achieve the near‐optimal weighted sum computation rate with very low complexity, which is tailored for the practical dynamic‐channel environment.

At present, magnetic induction switches are widely used in industrial automation control and biological sensing systems. A core module composed of a magnetic sensing device and a signal conditioning circuit is designed and analyzed in this paper. Utilizing a permalloy film with the anisotropic magneto-resistance (AMR) effect, the novel magnetic induction switch shows its ability to correctly detect the direction of magnetic fields. Furthermore, an interfacial circuit based on a trans-impedance amplifier (TIA) is designed to measure and regulate the output signal of the sensing device. Accurate simulation results show the gain of the TIA reaches up to 51.36 dB with a bandwidth of 1.3 GHz and a power consumption of 3.65 mW. The outstanding performance of the proposed module demonstrates the possibility of solving the problems induced by high input impedance, high frequency, and parasitic effects in magnetic induction switches.

Abstract Integrating backscatter communication (BackCom) into wireless powered communication networks (WPCNs) makes it possible to transmit information and harvest energy simultaneously, which is regarded as a promising method to enhance the throughput. A very important issue is how to allocate the charging time and transmitting time to efficiently utilize the energy in hybrid access point (HAP) so as to improve the network performance. This paper adopts convex optimization to handle the time allocation for both WPCNs and BackCom users. A two‐stage efficient algorithm is proposed to solve the problem of sum‐throughput maximization (STM) subject to the basic throughput requirements of BackCom users, where the Lagrange duality method is applied at the first‐stage, while golden section and bisection method are jointly used at the second‐stage. In order to solve the throughput unfairness among nodes in the STM problem, the common‐throughput maximization (CTM; i.e. the worst node's throughput) problem is further considered. This problem is decomposed into a master problem and a sub‐problem, which are solved in a progressive manner. Simulation results show that the proposed methods obtain substantial improvement compared to the benchmark scheme.

In this paper, an implementation method of high sensitivity thin film fiber optic pressure sensor based on air blowing method is proposed. The utility model is characterized in that a film ultra-thin microbubble structure is firstly formed on the surface of the AB epoxy glue solution by using high-pressure air, and then the film portion of the end portion of the microbubble is transferred to a single-mode fiber head with a hollow glass tube at the top. Finally, a thin film structure fiber Fabry-Perot fiber pressure sensor is formed. The experimental results show that the thickness of the thin film layer at the end of the FP sensor is only 16 μm. Correspondingly, the maximum pressure sensitivity can reach 263.15 pm/kPa. Meanwhile, due to the decrease of film thickness, the sensor's resistance to temperature interference is improved. The prepared fiber Fabry-Perot pressure sensor has the advantages of ultra-thin film thickness, high-pressure sensitivity, low cost and good repeatability. Therefore, it has good application value in high sensitivity pressure and sound wave detection.

Energy harvesting technology is regarded as a feasible solution for the continuous power supply of microelectronic devices. Efforts have been made to improve the output power of all kinds of energy harvesting devices. This paper reports a 2 × 2 array piezoelectric–electromagnetic hybrid energy harvester that achieves high power output through the combination of piezoelectric and electromagnetic conversion. The harvester included four piezoelectric–electromagnetic hybrid modules, each of which consisted of a piezoelectric sheet, a permanent magnet and a wound coil. The permanent magnet, also serving as the mass block of the cantilever beam when subjected to external stimulus, contributed to a large displacement of the vibration and generated high output power. At an acceleration of 1 g and a resonance frequency of 70.4 Hz, the measured maximum output power of the hybrid energy harvester was 66.08 mW, of which the piezoelectric and electromagnetic portions were 56.96 and 9.12 mW, respectively. Furthermore, in a charging experiment, a capacitor of 23.5 mF was charged to 11.5 V within 20 s, which demonstrates a practical application of the hybrid energy harvester for microelectronic devices.

The Age of Information (AoI) measures the freshness of information and is a critic performance metric for time-sensitive applications. In this paper, we consider a radio frequency energy-harvesting cognitive radio network, where the secondary user harvests energy from the primary users’ transmissions and opportunistically accesses the primary users’ licensed spectrum to deliver the status-update data pack. We aim to minimize the AoI subject to the energy causality and spectrum constraints by optimizing the sensing and update decisions. We formulate the AoI minimization problem as a partially observable Markov decision process and solve it via dynamic programming. Simulation results verify that our proposed policy is significantly superior to the myopic policy under different parameter settings.

This paper proposes a Fabry–Perot pressure sensor based on AB epoxy adhesive with ultra-high sensitivity under low pressure. Fabry–Perot interference, located between single-mode fiber (SMF) and hollow-core fiber (HCF), is an ultra-thin AB epoxy film formed by capillary action. Then the thick HCF was used to fix the HCF and SMF at both ends with AB epoxy adhesive. Experimental results show that when the thickness of AB epoxy film is 8.74 μm, and the cavity length is 30 μm, the sensor has the highest sensitivity. The sensitivity is 257.79 nm/MPa within the pressure range of 0–70 kPa. It also investigated the influence of the curing time of AB epoxy on the interference spectrum. Experiments showed that the interference spectrum peak is blue-shifted with the increase of curing time. Our study also demonstrated the humidity stability of this pressure sensor. These characteristics mean that our sensor has potential applications in the biomedical field and ocean exploration.

This paper investigates the secrecy communication in an underlay cognitive radio (CR) networks with one primary user (PU) as well as multiple PUs, where the radio frequency (RF) energy-harvesting secondary user (SU) transmits the confidential information to the destination in the presence of a potential eavesdropper. We introduce a RF energy-harvesting secondary jammer (SJ) to secure the SU transmissions. The system works in time slots, where each time slot is divided into the energy transfer (ET) phase and the information transfer (IT) phase. In ET phase, the SU and SJ capture energy from the PU transmissions; in the IT phase, the SU uses the harvested energy to transmit information to the destination without causing the harmful interference to the PU transmissions, while the SJ utilizes the captured energy to generate jamming signals to the eavesdropper to secure the SU transmissions. We aim to maximize the secrecy rate for SU transmissionsby jointly optimizing the time allocation between ET phase and IT phase and the transmit power allocation at the SU and SJ. We first formulate the secrecy rate maximization as non-convex optimization problems. Then, we propose efficient nested form algorithms for the non-convex problems. In the outer layer, we obtain the optimal time allocation by the one dimension search method. In the inner layer, we obtain the optimal transmit power allocation by the DC programming, where the Lagrange duality method is employed to solve the convex approximation problem. Simulation results verify that the proposed schemes essentially improve the secrecy rate of the secondary network as compared to the benchmark schemes.

Objective: To monitor iodized salt consumption and evaluate iodine deficiency status in 2014 in China.Materials and Methods: In 2014, a nationwide cross-sectional survey was conducted in 31 provinces (in this study, provinces, autonomous regions and municipalities in Mainland China were named as provinces). Probability proportional to size sampling method was adopted to recruit children ages 8–10 and pregnant women. 47,467 children’s and 18,994 pregnant women’s urine samples were collected and 47,706 children’s thyroid volumes were examined. Iodine content in salt was determined with 46,900 edible salt samples from children’s households; urinary iodine concentration (UIC) was tested from children and pregnant women’s urine samples; thyroid volume of children was assessed by ultrasound.Results: The national coverage rate of iodized salt and consumption rate of qualified iodized salt were 96.3% and 91.5%, respectively. Median iodine content in iodized salt was 25.2 mg/kg. In 22 of 31 provinces, the provincial coverage rates of iodized salt were over 95%. And consumption rates of qualified iodized salt were more than 90% in 21 provinces. In this study, the national median urinary iodine concentration (MUIC) of children in China was 197.9 μg/L. At the provincial level, MUIC of children in 19 provinces was 100–199 μg/L, which in 12 provinces was 200–299 μg/L. The national MUIC of pregnant women in 2014 was 154.6 μg/L, slightly higher than the lower limit of the WHO criteria for adequate (150–249 μg/L). At the provincial level, MUIC of pregnant women in 18 provinces was 100–149 μg/L, which in 13 provinces was 150–249 μg/L. The national prevalence of goiter among children in 2014 was 2.6%, of which only Shandong province (5.6%) exceeded the national standard (5%).Conclusions: In China, iodine deficiency disorders (IDD) has been eliminated since 2005. And in 2014, the IDD-free status still remained.

This paper presents a numerical and experimental study on the performance of a methanol steam reformer integrated with a hydrogen/air combustion reactor for hydrogen production. A CFD-based 3D model with mass and momentum transport and temperature characteristics is established. The simulation results show that better performance is achieved in the cross-U type reactor compared to either a tubular reactor or a parallel-U type reactor because of more effective heat transfer characteristics. Furthermore, Cu-based micro reformers of both cross-U and parallel-U type reactors are designed, fabricated and tested for experimental validation. Under the same condition for reforming and combustion, the results demonstrate that higher methanol conversion is achievable in cross-U type reactor. However, it is also found in cross-U type reactor that methanol reforming selectivity is the lowest due to the decreased water gas shift reaction under high temperature, thereby carbon monoxide concentration is increased. Furthermore, the reformed gas generated from the reactors is fed into a high temperature proton exchange membrane fuel cell (PEMFC). In the test of discharging for 4 h, the fuel cell fed by cross-U type reactor exhibits the most stable performance.

Though label propagation algorithm (LPA) is one of the fastest algorithms for community detection in complex networks, the problem of trivial solutions frequently occurring in the algorithm affects its performance. We propose a label propagation algorithm with prediction of percolation transition (LPAp). After analyzing the reason for multiple solutions of LPA, by transforming the process of community detection into network construction process, a trivial solution in label propagation is considered as a giant component in the percolation transition. We add a prediction process of percolation transition in label propagation to delay the occurrence of trivial solutions, which makes small communities easier to be found. We also give an incomplete update condition which considers both neighbor purity and the contribution of small degree vertices to community detection to reduce the computation time of LPAp. Numerical tests are conducted. Experimental results on synthetic networks and real-world networks show that the LPAp is more accurate, more sensitive to small community, and has the ability to identify a single community structure. Moreover, LPAp with the incomplete update process can use less computation time than LPA, nearly without modularity loss.

Human urinary disorders are generally studied in rodent models due to limitations of functional in vitro culture models of primary human urothelial cells (HUCs). Current HUC culture models are often derived from immortalized cancer cell lines, which likely have functional characteristics differ from healthy human urothelium. Here, we described a simple explant culture technique to generate HUCs and assessed their in vitro functions. Using transmission electron microscopy, we assessed morphology and heterogeneity of the generated HUCs and characterized their intercellular membrane structural proteins relative to ex vivo urothelium tissue. We demonstrated that our cultured HUCs are free of fibroblasts. They are also heterogeneous, containing cells characteristic of both immature basal cells and mature superficial urothelial cells. The cultured HUCs expressed muscarinic receptors (MR1 and MR2), carnitine acetyltransferase (CarAT), immunoregulatory cytokines IL7, IL15, and IL23, as well as the chemokine CCL20. HUCs also expressed epithelial cell-specific molecules essential for forming intercellular structures that maintain the functional capacity to form the physiological barrier of the human bladder urothelium. A subset of HUCs, identified by the high expression of CD44, expressed the Toll-like receptor 4 (TLR4) along with its co-receptor CD14. We demonstrated that HUCs express, at the mRNA level, both forms of the IL22 receptor, the membrane-associated (IL22RA1) and the secreted soluble (IL22RA2) forms; in turn, IL22 inhibited expression of MR1 and induced expression of CarAT and two antimicrobial peptides (S100A9 and lipocalin-2). While the cellular sources of IL22 have yet to be identified, the HUC cytokine and chemokine profiles support the concept that IL22-producing cells are present in the human bladder mucosa tissue and that IL22 plays a regulatory role in HUC functions. Thus, the described explant technique is clearly capable of generating functional HUCs suitable for the study of human urinary tract disorders, including interactions between urothelium and IL22-producing cells.