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Abstract Apoptosis plays a pivotal role in pathogen elimination and maintaining homeostasis. However, viruses have evolved strategies to evade apoptosis, enabling their persistence within the host. Z-DNA binding protein 1 (ZBP1) is a potent innate immune sensor that detects cytoplasmic nucleic acids and activates the innate immune response to clear pathogens. When apoptosis is inhibited by viral invasion, ZBP1 can be activated to compensate for the effect of apoptosis by triggering an innate immune response. This review examined the mechanisms of apoptosis inhibition and ZBP1 activation during viral invasion. The authors outlined the mechanisms of ZBP1-induced type I interferon, pyroptosis and necroptosis, as well as the crosstalk between ZBP1 and the cGAS-STING signalling pathway. Furthermore, ZBP1 can reverse the suppression of apoptotic signals induced by viruses. Intriguingly, a positive feedback loop exists in the ZBP1 signalling pathway, which intensifies the innate immune response while triggering a cytokine storm, leading to tissue and organ damage. The prudent use of ZBP1, which is a double-edged sword, has significant clinical implications for treating infections and inflammation.

IntroductionThe Coronavirus disease 2019 (COVID-19) pandemic is a global traumatic event that has profoundly struck individuals’ mental health. However, this might potentially promote positive transformation such as posttraumatic growth (PTG). Studies have indicated that the COVID-19 pandemic negatively affected the well-being of resident physicians, but little is known about PTG among this vulnerable population in China. Therefore, this study investigated the prevalence and associated factors of PTG among Chinese resident physicians after 3-years outbreak of COVID-19.MethodsAn online survey was conducted from 9 March to 20 March in 2023. PTG was assessed using the 10-item Posttraumatic Growth Inventory-Short Form (PTGI-SF). Scores ≥30 implied moderate-to-high PTG. We also collected possible associated factors for PTG, including socio-demographic and psychological variables. Data was analyzed by applying descriptive statistics, univariable and multivariable logistic regression models.ResultsIn total, 2267 Chinese resident physicians provided validated data. 38.7% of them reported moderate-to-high PTG. In the multivariable logistic regression models, age (odds ratio, OR = 1.039; 95% confidence interval, 95%CI = 1.008–1.070), female (OR = 1.383, 95%CI = 1.151–1.662), satisfied or neutral with annual income (OR = 2.078, 95%CI = 1.524–2.832; OR = 1.416, 95%CI = 1.157–1.732), sufficient support at work (OR = 1.432, 95%CI = 1.171–1.751) and resilience (OR = 1.171, 95%CI = 1.096–1.252) were significantly positively associated with moderate-to-high PTG. On the contrary, burnout (OR = 0.653, 95%CI = 0.525–0.812), depression symptoms (OR = 0.700, 95%CI = 0.552–0.889), and stress (OR = 0.757, 95%CI = 0.604–0.949) were significantly negatively associated with moderate-to-high PTG.DiscussionOverall, resident physicians in China experienced relatively high prevalence of PTG that could be associated with several psychosocial factors. Findings may provide evidence to develop interventions for resident physicians to systematically and constructively process traumatic events related to the pandemic and foster their PTG.

For the typical magnetically coupled wireless power transmission system, power consumption is often caused by the impedance effect of the primary and secondary sides, the loss and heat generation of the actual semiconductor devices and the change of the coupling coefficient caused by distance. The uncertainty of frequency tracking efficiency caused by high enough coil transmission with low load receiving efficiency and load impedance changes makes it difficult to stabilize the output of the optimal efficiency point of the system. In order to solve this problem, a wireless power transmission system is proposed based on the traditional wireless power transmission system a wireless power transmission system is proposed to predict and stabilize the variable impedance, so as to stably track the efficiency of the system in the resonant frequency range. It is not affected by load jitter, actual circuit parameters and environmental disturbance. The test results show that the transmission efficiency of tracking frequency and predicting impedance can be realized at a certain distance, and the difference between the prediction error and the actual efficiency is about 1%–3%.

The long-term use of polyethylene mulch (PEM) films can cause plastic film residual pollution that has an adverse effect on soil health and crop quality. To address this issue, poly(butylene adipate-co-terephthalate) (PBAT), an aliphatic–aromatic copolyester, is widely used in the production of commercially biodegradable plastic mulch (BDM) films. The use of BDMs can alleviate soil plastic pollution and reduce the labor cost of retrieving plastic film residues from the field. The effects of BDM and PEM on the agronomic and nutritional quality traits of cabbage cultivar ‘Sugan No. 35’, as well as the physicochemical properties and microbial communities of the soil were analyzed during two consecutive years of the experiment. No significant difference was observed in the cabbage agronomic and nutritional quality traits among three mulching treatments. Nonetheless, the mulching and degradation of BDM reduced the pH value and increased the organic matter content of the soil samples compared with PEM mulching. In the soil bacterial and fungal communities, Proteobacteria and Ascomycota were the most abundant bacterial phylum and fungal phylum across all the soil samples, respectively; the use of BDM increased the relative abundance of soil Proteobacteria and Ascomycota compared with PEM mulching. The overall cost of BDM mulching was much lower than that of PEM mulching during the cabbage production.

Fusarium oxysporum f. sp. conglutinans is the causal agent of Fusarium wilt of cabbage (Brassica oleracea var. capitata L.), which results in severe yield loss. Here, we report a high-quality genome sequence of a race 1 strain (IVC-1) of F. oxysporum f. sp. conglutinans, which was assembled using a combination of PacBio long-read and Illumina short-read sequences. The assembled IVC-1 genome has a total size of 71.18 Mb, with a contig N50 length of 4.59 Mb, and encodes 23,374 predicted protein-coding genes. The high-quality genome of IVC-1 provides a valuable resource for facilitating our understanding of F. oxysporum f. sp. conglutinans–cabbage interaction.

BackgroundThe inverted classroom model differs from the traditional teaching model as it reverses the pattern of knowledge transfer and internalization. In recent years, this new teaching model has received much attention in undergraduate medical education. Pathophysiology is a course in the undergraduate Chinese medical curriculum that is critical in bridging basic medical science and clinical medicine. ObjectiveThe purpose of this study was to investigate the application of inverted classroom in delivering the course on pathophysiology to Chinese undergraduate medical students. MethodsIn the spring semester of 2018, inverted classroom teaching was implemented for second-year clinical medicine students at the College of Medicine at Nanchang University. The topics of hypoxia and respiratory failure were selected for the inverted classroom study. The effect of the inverted classroom on teaching pathophysiology was evaluated using classroom performance metrics, a final examination, and questionnaires. ResultsThis study found that students in the inverted classroom group achieved higher scores in their in-course assessments (82.35 [SD 11.45] vs 81.33 [SD 9.51], respectively) and in their final exams (73.41 [SD 10.37] vs 71.13 [SD 11.22], respectively) than those in the traditional lecture-based group, but the scores were not significantly different (P=.13, unpaired two-tailed t test). There was also no significant difference in the distribution of the score segments in the class quiz (P=.09, chi-square test) and in the final exams (P=.25, chi-square test) between the 2 groups. Further, most of the students reported that the inverted classroom increased their learning motivation, made them more confident, and helped them understand the content on pathophysiology better. The students in the inverted classroom also improved in their problem-solving skills and teamwork abilities. However, some students from the inverted classroom group also reported that the self-learning and preparatory work before class increased their learning burden. ConclusionsThis study shows the feasibility and promise of inverted classroom for teaching pathophysiology to undergraduate Chinese medical students. The inverted classroom improves students’ learning interests and attitudes toward learning. However, further studies are required to assess the benefits of broader acceptance and implementation of the inverted classroom among Chinese undergraduate medical students.

In present-day mechanized garlic seeding, high missing rates and low qualified percentages of single seeds are common problems; thus, a finger clip plate garlic seed-metering device was designed in this study. First, the structure and working principle of the seed-metering device were studied. Subsequently, the critical component parameters of the seed-metering device were determined using theoretical calculations; then, EDEM software was used in single-factor simulation experiments to analyze the effects of opening the diameter of the seed-collecting spoon, the operating speed of the seeding tray, and the population number on the seed-filling performance. Finally, a Box–Behnken center combination experiment was conducted with the population, opening the diameter of the seed-collecting spoon, and rotating the speed of the seeding tray as experiment factors, with the single-seed filling rate, qualified percent, and missing rate as evaluation indicators. A three-factor and three-level orthogonal test was conducted to establish the mathematic regression model of the experiment factors and evaluation indicators, as well as to realize the parameter optimization. After rounding, the laboratory validation test was carried out with 240~280 seeds, a 26 mm seed scoop-opening diameter, and a 28 r/min operating speed. The average qualified rate, missing rate, and reply rate of single seeds were 91.86%, 2.71%, and 5.43%, respectively, which is basically consistent with the forecast results of regression model.

In view of the harsh conditions of rapid charging and discharging of electric vehicles, a hybrid lithium-ion battery thermal management system combining composite phase change material (PCM) with liquid cooling was proposed. Based on the numerical heat transfer model, a simulation experiment for the battery thermal management system was carried out. Taking the maximum temperature and temperature difference of the battery module as the objectives, the effects of PCM thickness, the liquid flow rate and the cross-sectional area of the liquid channel on the temperature of the battery module were analyzed using response surface methodology (RSM). The results show that 31 groups of candidate parameter combinations can be obtained through response surface analysis, and phase change material (PCM) thickness should be minimized in order to improve space utilization in the battery module. The optimal parameter combination is a flow rate of 0.4 m/s and a PCM thickness of 5.58 mm, with the cross-sectional area of the liquid channel as 3.35 mm2.

Successful engraftment of hematopoietic stem cells (HSCs) and progenitor cells (HSPCs) may be considered as a basis for the repopulation of the blood cells after transplantation in adults. Therefore, in vivo and ex vivo expansion of HSCs holds great promise for clinical applications. In this review, the mechanisms of HSC expansion will be discussed, considering the previous studies and works of literature. This is aimed to identify the signaling pathways that regulate HSC expansion and improve the application of engraftment in disease management. The following aspects will be included: (i) Stimulation of HSCs growth in vivo through gene regulation and cytokines activation; (ii) direct or indirect induction of HSC expansion by regulating signaling pathways; (iii) addition to assisting cells to help in the proliferation of HSCs; (iv) changing of living environment in the HSCs cultures via adjusting components and forms of cultures; (v) enhancement of HSC expansion by incorporating substances, such as extracellular vesicles (EVs), UM171, among others. In this review, recent new findings that provide us with new insights into HSC expansion methods have been summarized. Furthermore, these findings will also provide more possibilities for the development of some novel strategies for expanding and engrafting HSCs applied for treatments of some hematopoietic disorders.

This paper proposes TOD-MUSIC algorithm to improve the Direction of Arrival (DOA) estimation accuracy under low Signal-to-Noise Ratio (SNR) in transmit-only diversity bistatic Multiple-Input Multiple-Output (MIMO) radar. Firstly, the TOD-MUSIC algorithm uses Toeplitz matrix reconstruction to make it possible to process the correlated sources. Then it uses the linear operation to reduce the computational complexity. Finally, the physical properties of spectral peak (which are not continuous functions and approach a larger value at the DOAs) is utilized to make the spectral peak more obvious. It is proved that the TOD-MUSIC algorithm can effectively improve the DOA estimation accuracy. We reveal that the TOD-MUSIC algorithm can not only process correlated sources, but also reduce calculation amount by solving the DOA estimation ambiguity problem under the under-sampling. The simulation results show that: the TOD-MUSIC algorithm makes the DOA estimation accuracy improved by 2° at low SNR, approaching to MUSIC algorithm at high SNR.

Transmission in non-line-of-sight (NLOS) conditions has a poor throughput in device-to-device (D2D) communications. In order to achieve high throughput, adaptive modulation has been selected as a spectrally-efficient transmission technology. Moreover, quantifying the performance of a fading channel in NLOS conditions becomes a core issue. In general, the $\eta -\mu $ distribution can be employed to characterize and model the NLOS fading channel. Effective evaluating the quality of $\eta -\mu $ channels can provide an efficient theoretical reference for determining the optimum switching thresholds in adaptive modulation. However, due to the sensitivity of D2D transmission to fading channels, providing an efficient theoretical benchmark for evaluating, the quality of $\eta -\mu $ channels is still a challenge in transmission design. In this paper, the nondata-aided error vector magnitude (NDA-EVM) is considered as a novel metric to evaluate the quality of wireless fading channels. Specifically, the NDA-EVM of the multilevel quadrature amplitude modulation (MQAM) signals over $\eta -\mu $ fading channels and its lower bound is analytically derived. This can be used to further determine the optimum switching thresholds in adaptive modulation in NLOS conditions. The numerical results validate the effectiveness of the proposed formulation and also reveal the influence of the channel parameters on the lower bound of the NDA-EVM.

The long-term sensor drift phenomenon seriously restricts the performance of Electronic Nose (E-nose) systems in their various applications. Due to frequent recalibrations, traditional drift compensation methods are costly and laborious, and their performance are limited due to the nonlinear dynamic properties of the drift. The latest proposed Broad Learning System (BLS) has been confirmed to be an efficient and effective learning technique for many machine learning problems. However, BLS with cross-domain learning capability has rarely been studied. In this paper, a novel unified framework called Domain Transfer Broad Learning System (DTBLS) is proposed based on BLS, to address the issue of drift via adaptive compensation. For the case where there is no labeled target sample, with simultaneous considerations of the empirical loss of source data, marginal distribution adaptation, conditional distribution adaptation and manifold regularization, the DTBLS framework learns a robust target classifier by using labeled source data and unlabeled target data to compensate the drift of sensor response adaptively. To the best of our knowledge, DTBLS is the first BLS-based transfer learning framework for the problem of dataset shift existing in E-nose systems. Like the basic BLS, high computation efficiency is achieved due to the existence of analytical solution. Parameter sensitivity analysis is also conducted to show that the optimal solution can be obtained in a wide range. Experiments on a public gas sensor drift dataset demonstrate that the proposed method outperforms the state-of-the-art methods well.

MiRNAs (microRNAs) are the most abundant family of small noncoding RNAs in mammalian cells. Increasing evidence shows that miRNAs are crucial regulators of individual development and cell homeostasis by controlling various biological processes. Therefore, miRNA dysfunction can lead to human diseases, especially in cancers with high morbidity and mortality worldwide. MiRNAs play different roles in these processes. In recent years, studies have found that miR-424-5p is closely related to the occurrence, development, prognosis and treatment of tumors. This review discusses how miR-424-5p plays a role in different kinds of cancers from different stages of tumors, including its roles in (i) promoting or inhibiting tumorigenesis, (ii) regulating tumor development in the tumor microenvironment and (iii) participating in cancer chemotherapy. This review provides a deep discussion of the latest findings on miR-424-5p and its importance in cancer, as well as a mechanistic analysis of the role of miR-424-5p in various tissues through target gene verification and pathway analysis.

Plastic film mulching is a common practice to increase vegetable yields. However, the effects of plastic mulch films on different vegetable fields remain largely unknown. In the current study, biodegradable mulch films (BDMs) and polyethylene mulch films (PEMs) were used to explore their effects on three vegetable fields (broccoli, chili pepper and garlic). Compared with bare soil, both BDMs and PEMs could effectively maintain higher exchangeable potassium contents in rhizosphere soil, and significantly increase various soil properties and microbial abundance both in rhizosphere and non-rhizosphere soils. In terms of yields, the treatments of BDMs and PEMs effectively increased broccoli, chili pepper and garlic yields by 17.6–19.0%, 21.3–23.3% and 26.0–29.5%, respectively, when compared with the bare soil. Our initial characterization provided insights into the effects of BMDs and PEMs on soil properties, yields and diversity of rhizosphere and non-rhizosphere microbial communities in three vegetable fields.

Abstract Background Diagnosis of Alzheimer’s disease (AD) is very important, and MRI is an effective imaging mode of Alzheimer’s disease. There are many existing studies on the diagnosis of Alzheimer’s disease based on MRI data. However, there are no studies on the transfer learning between different datasets (including different subjects), thereby improving the sample size of target dataset indirectly. Methods Therefore, a new framework method is proposed in this paper to solve this problem. First, gravity transfer is used to transfer the source domain data closer to the target data set. Secondly, the best deviation between the transferred source domain samples and the target domain samples is searched by instance transfer learning algorithm (ITL) based on wrapper mode, thereby obtaining optimal transferred domain samples. Finally, the optimal transferred domain samples and the target domain training samples are combined for classification. If the source data and the target data have different features, a feature growing algorithm is proposed to solve this problem. Results The experimental results show that the proposed method is effective regardless of different kernel functions, different number of samples and different parameters. Besides, the transferred source domain samples by ITL algorithm can enlarge the target domain training samples and assist to improve the classification accuracy significantly. Conclusions Therefore, the study can enlarge the samples of AD by instance transfer learning, thereby being helpful for the small sample problems of AD. Since the proposed algorithm is a framework algorithm, the study is heuristics to the relevant researchers.

The friend leukemia integration 1 (Fli-1) gene is involved in the expression control of key genes in multiple pathogenic/physiological processes, including cell growth, differentiation, and apoptosis; this implies that Fli-1 is a strong candidate for drug development. In our previous study, a 3′,5′-diprenylated chalcone, (E)-1-(2-hydroxy-4-methoxy-3,5-diprenyl) phenyl-3-(3-pyridinyl)-propene-1-one (C10), was identified as a novel anti-prostate cancer (PCa) agent. Here, we investigated the molecular mechanisms underlying the anti-cancer effects of C10 on the growth, metastasis, and invasion of PC3 cells in vitro. Our results show that C10 exhibited a strong inhibitory effect on proliferation and metastasis of PC3 cells via several cellular and flow cytometric analyses. Further mechanism studies revealed that C10 likely serves as an Fli-1 agonist for regulating the expression of Fli-1 target genes including phosphatidylinositol 3-kinase (P110), murine double minute2 (MDM2), B-cell lymphoma-2 (Bcl-2), Src homology-2 domain-containing inositol 5-phosphatase 1 (SHIP-1), and globin transcription factor-1 (Gata-1) as well as the phosphorylation of extracellular-regulated protein kinases 1 (ERK1). Further, we confirmed that C10 can regulate the expressions of vascular endothelial growth factor 1 (VEGF-1), transforming growth factor-β2 (TGF-β2), intercellular cell adhesion molecule-1 (ICAM-1), p53, and matrix metalloproteinase 1 (MMP-1) genes associated with tumor apoptosis, migration, and invasion. Thus, C10 exhibits stronger anticancer activity with novel molecular targets and regulatory molecular mechanisms, indicating its great potency for development as a novel targeted anticancer drug.

Clustering techniques in wireless sensor networks have been widely utilized for their good performance in reducing energy dissipation and prolonging network lifetime. Once the cluster heads have been decided, the allocation of member nodes in the cross coverages formed by two or more clusters is critical to keep an energy balance on the cluster heads. In earlier studies, however, the allocation of member nodes simply depends on the distance or degree (the node number within the cluster heads’ communication radius) and, therefore, could cause imbalance to the cluster heads’ load and further degrade the whole wireless sensor network. To maintain the load balance of the cluster heads, in this article, game theory is introduced into the allocation problem of the member nodes. Before using the game theory approach, the number and distribution of cluster heads are first checked. If the cover rate of the cluster heads is low, then the node(s) uncovered by any cluster are randomly selected as new cluster head(s) to attain the cover rate required in the article. Furthermore, the number of cluster heads in a monitoring region is restricted. Finally, a game-based, energy-balance method is proposed and applied in the cluster-based routing protocols to improve their performance. For verification, the proposed method is embedded into the localized game theoretical clustering algorithm and hybrid, game theory–based and distributed clustering algorithm, which are two game theory and typical cluster-based routing protocols. The experimental results show that both of the improved protocols do balance the loads of the cluster heads and achieve better performance than their original versions in spanning the lifetime and balancing the energy in wireless sensor networks.

Emergency public event arises everyday on social network. The information propagation of emergency public event (favorable and harmful) is researched. The dynamics of a susceptible-infected-susceptible and susceptible-infected-removed epidemic models incorporated with information propagation of emergency public event are studied. In particular, we investigate the propagation model and the infection spreading pattern using nonlinear dynamic method and results obtained through extensive numerical simulations. We further generalize the model for any arbitrary number of infective network nodes to mimic existing scenarios in online social network. The simulation results reveal that the inclusion of multiple infective node achieved stability and equilibrium in the proposed information propagation model.

In order to analyze the channel estimation performance of near space high altitude platform station (HAPS) in wireless communication system, the structure and formation of HAPS are studied in this paper. The traditional Least Squares (LS) channel estimation method and Singular Value Decomposition-Linear Minimum Mean-Squared (SVD-LMMS) channel estimation method are compared and investigated. A novel channel estimation method and model are proposed. The channel estimation performance of HAPS is studied deeply. The simulation and theoretical analysis results show that the performance of the proposed method is better than the traditional methods. The lower Bit Error Rate (BER) and higher Signal Noise Ratio (SNR) can be obtained by the proposed method compared with the LS and SVD-LMMS methods.

In the inductive power transfer (IPT) system, it is recommended to drive the resonant inverter in zero-voltage switching (ZVS) or zero-current switching (ZCS) operation to reduce switching losses, especially in dynamic applications with variable couplings. This paper proposes an improved autonomous current-fed push-pull parallel-resonant inverter, which not only realizes the ZVS operation by tracking the zero phase angle (ZPA) frequency, but also improves the output power and overall efficiency in a wide range by reducing gate losses and switching losses. The ZPA frequencies characteristic of the parallel-parallel resonant circuit in both bifurcation and bifurcation-free regions is derived and verified by theory and experiments, and the comparative experimental results demonstrate that the improved inverter can significantly increase the output power from 7.68 W to 8.74 W and has an overall efficiency ranging from 63.5% to 72.5% compared with the traditional inverter at a 2 cm coil distance. Furthermore, with a 2-fold input voltage (24 V), the improved inverter can achieve an approximate 4-fold output power of 38.9 W and overall efficiency of 83.6% at a 2 cm coil distance.

Human umbilical cord-derived mesenchymal stem cells (UCMSCs) are particularly attractive cells for cellular and gene therapy in acute liver failure (ALF). However, the efficacy of this cell therapy in animal studies needs to be significantly improved before it can be translated into clinics. In this study, we investigated the therapeutic potential of UCMSCs that overexpress hepatocyte growth factor (HGF) in an acetaminophen-induced acute liver failure mouse model. We found that the HGF-UCMSC cell therapy protected animals from acute liver failure by reducing liver damage and prolonging animal survival. The therapeutic effect of HGF-UCMSCs was associated with the increment in serum glutathione (GSH) and hepatic enzymes that maintain redox homeostasis, including γ-glutamylcysteine synthetase (γ-GCS), superoxide dismutase (SOD), and catalase (CAT). Immunohistochemical staining confirmed that HGF-UCMSCs were mobilized to the injured areas of the liver. Additionally, HGF-UCMSCs modulated apoptosis by upregulating the antiapoptotic Bcl2 and downregulating proapoptotic genes, including Bax and TNFα. Taken together, these data suggest that ectopic expression of HGF in UCMSCs protects animals from acetaminophen-induced acute liver failure through antiapoptosis and antioxidation mechanisms.

The high demand for multimedia applications in environmental monitoring, invasion detection, and disaster aid has led to the rise of wireless sensor network (WSN). With the increase of reliability and diversity of information streams, the higher requirements on throughput and quality of service (QoS) have been put forward in data transmission between two sensor nodes. However, lower spectral efficiency becomes a bottleneck in non-line-of-sight (NLOS) transmission of WSN. This paper proposes a novel nondata-aided error vector magnitude based adaptive modulation (NDA-EVM-AM) to solve the problem. NDA-EVM is considered as a new metric to evaluate the quality of NLOS link for adaptive modulation in WSN. By modeling the NLOS scenario as the η − μ fading channel, a closed-form expression for the NDA-EVM of multilevel quadrature amplitude modulation (MQAM) signals over the η − μ fading channel is derived, and the relationship between SER and NDA-EVM is also formulated. Based on these results, NDA-EVM state machine is designed for adaptation strategy. The algorithmic complexity of NDA-EVM-AM is analyzed and the outage capacity of NDA-EVM-AM in an NLOS scenario is also given. The performances of NDA-EVM-AM are compared by simulation, and the results show that NDA-EVM-AM is an effective technique to be used in the NLOS scenarios of WSN. This technique can accurately reflect the channel variations and efficiently adjust modulation order to better match the channel conditions, hence, obtaining better performance in average spectral efficiency.

The spectrum allocation for cognitive radio sensor networks (CRSNs) has received considerable research attention under the assumption that the spectrum environment is static. However, in practice, the spectrum environment varies over time due to primary user/secondary user (PU/SU) activity and mobility, resulting in time-varied spectrum resources. This paper studies resource allocation for chunk-based multi-carrier CRSNs with time-varied spectrum resources. We present a novel opportunistic capacity model through a continuous time semi-Markov chain (CTSMC) to describe the time-varied spectrum resources of chunks and, based on this, a joint power and chunk allocation model by considering the opportunistically available capacity of chunks is proposed. To reduce the computational complexity, we split this model into two sub-problems and solve them via the Lagrangian dual method. Simulation results illustrate that the proposed opportunistic capacity-based resource allocation algorithm can achieve better performance compared with traditional algorithms when the spectrum environment is time-varied.

Conventional approaches for designing antennas are often time‐consuming and computationally expensive processes. In this letter, a time and resource‐efficient inverse design method for the multiparameter antenna is proposed which is capable of generating an effective dataset using the hybrid machine learning method. The proposed method filters the design variables and predicts the secondary variables based on the choice of primary design variables. The generated training dataset is used for the effective prediction of design variables based on multiple performance metrics. The proposed machine learning model utilized autoencoder for dimensionality reduction of multiple performance metrics and support vector regression is used for the prediction of design variables. To prove the effectiveness of the proposed model, a metasurface‐loaded low‐profile antenna is considered as a design example. [ABSTRACT FROM AUTHOR]