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Abstract Osteosarcoma (OSA) is a primary bone malignancy characterized by its aggressive nature and high propensity for metastasis. Despite advancements in multimodal therapies, the clinical outcomes for OSA patients remain suboptimal, necessitating deeper molecular insights for improved therapeutic strategies. Here, we employed single-cell RNA sequencing (scRNA-seq) to elucidate the cellular heterogeneity and transcriptional dynamics of OSA tumors. Our study identified eleven distinct tumor cell subpopulations, including osteoblastic, chondroblastic, and myeloid lineages, each exhibiting unique transcriptional profiles associated with disease progression and metastasis. Epithelial-mesenchymal transition (EMT) emerged as a critical process driving aggressive phenotypes, supported by gene set enrichment analyses (GSVA) and transcription factor regulatory network analyses. Integration of copy number variation (CNV) data highlighted genomic alterations in osteoblastic and chondroblastic cells, implicating potential therapeutic targets. Furthermore, immune cell infiltration analyses revealed distinct immune profiles across OSA subtypes, correlating with tumor mutational burden (TMB) and clinical outcomes. Our findings underscore the complexity of OSA biology and provide a foundation for developing personalized treatment strategies targeting tumor heterogeneity and immune interactions.

BackgroundFall detection is of great significance in safeguarding human health. By monitoring the motion data, a fall detection system (FDS) can detect a fall accident. Recently, wearable sensors–based FDSs have become the mainstream of research, which can be categorized into threshold-based FDSs using experience, machine learning–based FDSs using manual feature extraction, and deep learning (DL)–based FDSs using automatic feature extraction. However, most FDSs focus on the global information of sensor data, neglecting the fact that different segments of the data contribute variably to fall detection. This shortcoming makes it challenging for FDSs to accurately distinguish between similar human motion patterns of actual falls and fall-like actions, leading to a decrease in detection accuracy. ObjectiveThis study aims to develop and validate a DL framework to accurately detect falls using acceleration and gyroscope data from wearable sensors. We aim to explore the essential contributing features extracted from sensor data to distinguish falls from activities of daily life. The significance of this study lies in reforming the FDS by designing a weighted feature representation using DL methods to effectively differentiate between fall events and fall-like activities. MethodsBased on the 3-axis acceleration and gyroscope data, we proposed a new DL architecture, the dual-stream convolutional neural network self-attention (DSCS) model. Unlike previous studies, the used architecture can extract global feature information from acceleration and gyroscope data. Additionally, we incorporated a self-attention module to assign different weights to the original feature vector, enabling the model to learn the contribution effect of the sensor data and enhance classification accuracy. The proposed model was trained and tested on 2 public data sets: SisFall and MobiFall. In addition, 10 participants were recruited to carry out practical validation of the DSCS model. A total of 1700 trials were performed to test the generalization ability of the model. ResultsThe fall detection accuracy of the DSCS model was 99.32% (recall=99.15%; precision=98.58%) and 99.65% (recall=100%; precision=98.39%) on the test sets of SisFall and MobiFall, respectively. In the ablation experiment, we compared the DSCS model with state-of-the-art machine learning and DL models. On the SisFall data set, the DSCS model achieved the second-best accuracy; on the MobiFall data set, the DSCS model achieved the best accuracy, recall, and precision. In practical validation, the accuracy of the DSCS model was 96.41% (recall=95.12%; specificity=97.55%). ConclusionsThis study demonstrates that the DSCS model can significantly improve the accuracy of fall detection on 2 publicly available data sets and performs robustly in practical validation.

Objective: Objectively and efficiently measuring physical activity is a common issue facing the fields of medicine, public health, education, and sports worldwide. In response to the problem of low accuracy in predicting energy consumption during human motion using accelerometers, a prediction model for asynchronous energy consumption in the human body is established through various algorithms, and the accuracy of the model is evaluated. The optimal energy consumption prediction model is selected to provide theoretical reference for selecting reasonable algorithms to predict energy consumption during human motion.Methods: A total of 100 subjects aged 18–30 years participated in the study. Experimental data for all subjects are randomly divided into the modeling group (n = 70) and validation group (n = 30). Each participant wore a triaxial accelerometer, COSMED Quark pulmonary function tester (Quark PFT), and heart rate band at the same time, and completed the tasks of walking (speed range: 2 km/h, 3 km/h, 4 km/h, 5 km/h, and 6 km/h) and running (speed range: 7 km/h, 8 km/h, and 9 km/h) sequentially. The prediction models were built using accelerometer data as the independent variable and the metabolic equivalents (METs) as the dependent variable. To calculate the prediction accuracy of the models, root mean square error (RMSE) and bias were used, and the consistency of each prediction model was evaluated based on Bland–Altman analysis.Results: The linear equation, logarithmic equation, cubic equation, artificial neural network (ANN) model, and walking-and-running two-stage model were established. According to the validation results, our proposed walking-and-running two-stage model showed the smallest overall EE prediction error (RMSE = 0.76 METs, Bias = 0.02 METs) and the best performance in Bland–Altman analysis. Additionally, it had the lowest error in predicting EE during walking (RMSE = 0.66 METs, Bias = 0.03 METs) and running (RMSE = 0.90 METs, Bias < 0.01 METs) separately, as well as high accuracy in predicting EE at each single speed.Conclusion: The ANN-based walking-and-running two-stage model established by separating walking and running can better estimate the walking and running EE, the improvement of energy consumption prediction accuracy will be conducive to more accurate to monitor the energy consumption of PA.

Abstract Piezoelectric polymers have been widely used in a variety of applications, including tactile sensors, energy harvesting, polymer actuators, and biological devices. In this investigation, we fabricated the PLLA (poly(L‐lactic Acid)‐based bimorph structures for piezoelectric motor applications. First, a strain measurement setup was established to measure the strain of the PLLA and PDLA film at different temperatures. The effective piezoelectric constants d14 calculated for the PLLA and PDLA films were 10.2 pC/N and 9.4 pC/N, respectively. Furthermore, the PLLA films showed a strong thermostability of piezoelectricity at 130°C. The PLLA bimorph minimotor showed the maximum load of 1.2 g and the maximum torque of 0.019 cN·m. The minimotors were capable of rotating a plastic hemisphere container in the clockwise and counterclockwise directions at 65 V voltage. Due to the light weight, low driving voltage, and thermal stability, the PLLA/PDLA motors show a great promise in future Braille display, robots, and mini‐digital camera applications.

Ecosystem service (ES) flow is an indispensable link between natural ecosystem and socioeconomic system. Research on ES flow is of great significance for regional ecosystem management and ecological compensation policies. However, previous studies have mainly focused on ES flow within a region or ES flow across regions, without distinguishing the relationship between ES flow within a region and ES flow from the region to its external regions. Therefore, taking the Huangshui River Basin in the Tibetan Plateau as an example, we combined the revised equivalent factor method and the breaking point model to analyze the ES flow within the basin and ES flow between the basin and its external regions (including adjacent and distant systems) under the metacoupling framework. The results showed that ES flow within the basin, ES flow to adjacent systems and ES flow to distant systems decreased from 2000 to 2020. ES flow to distant systems decreased the most, with an average decrease of 666 million yuan. Moreover, among different ESs, the decrease in hydrological regulation flow to the distant system was the most substantial, at least 2.4 times that of other ESs. Grassland loss in the basin was the main reason for the decrease in hydrological regulation flow to distant systems. Therefore, we suggest that future ecosystem conservation in the basin needs the participation of distant systems to ensure the ESs provided to them. Following the basic principle of “who benefits, who compensates”, distant systems that benefit can establish the grassland ecological compensation mechanisms to maintain the hydrological regulation service flowing to them, thereby ensuring water security and promoting sustainable development.

Abstract Background For a healthy parturient, a cardiopulmonary collapse that suddenly occurs shortly after an uneventful caesarean section is a relatively rare event and presents a significant challenge for the anesthesia provider. Case presentation Amniotic fluid embolism (AFE) is characterized by acute and rapid collapse and is well known to the obstetric team. Our patient experienced sudden cardiovascular collapse, severe respiratory difficulty and hypoxia, in the absence of other explanations for these findings at the time, and thus AFE was immediately become the focus of the consideration. However, there is no quick, standard laboratory test for AFE, therefore the diagnosis is one of exclusion based on presenting symptoms and clinical course. After given symptomatic treatment, the patient made an uneventful initial recovery in a short period and developed a rash. We recognized that the postpartum shock was associated with delayed anaphylaxis of antibiotics. Conclusions These observations have implications for understanding whenever administering drugs in surgery, which may affect the anesthesiologist’s judgment regarding the complications of anesthesia. Even though serious complications of common perioperative drugs may rarely occur, anesthesia providers should be aware of the consideration. Early recognition and effective treatment are more important than prompt diagnosis.

BackgroundRheumatoid arthritis (RA), a chronic autoimmune inflammatory disease, is often characterized by persistent morning stiffness, joint pain, and swelling. Early diagnosis and timely treatment of RA can effectively delay the progression of the condition and significantly reduce the incidence of disability. In the study, we explored the function of pyroptosis-related genes (PRGs) in the diagnosis and classification of rheumatoid arthritis based on Gene Expression Omnibus (GEO) datasets.MethodWe downloaded the GSE93272 dataset from the GEO database, which contains 35 healthy controls and 67 RA patients. Firstly, the GSE93272 was normalized by the R software “limma” package. Then, we screened PRGs by SVM-RFE, LASSO, and RF algorithms. To further investigate the prevalence of RA, we established a nomogram model. Besides, we grouped gene expression profiles into two clusters and explored their relationship with infiltrating immune cells. Finally, we analyzed the relationship between the two clusters and the cytokines.ResultCHMP3, TP53, AIM2, NLRP1, and PLCG1 were identified as PRGs. The nomogram model revealed that decision-making based on established model might be beneficial for RA patients, and the predictive power of the nomogram model was significant. In addition, we identified two different pyroptosis patterns (pyroptosis clusters A and B) based on the 5 PRGs. We found that eosinophil, gamma delta T cell, macrophage, natural killer cell, regulatory T cell, type 17 T helper cell, and type 2 T helper cell were significant high expressed in cluster B. And, we identified gene clusters A and B based on 56 differentially expressed genes (DEGs) between pyroptosis cluster A and B. And we calculated the pyroptosis score for each sample to quantify the different patterns. The patients in pyroptosis cluster B or gene cluster B had higher pyroptosis scores than those in pyroptosis cluster A or gene cluster A.ConclusionIn summary, PRGs play vital roles in the development and occurrence of RA. Our findings might provide novel views for the immunotherapy strategies with RA.

BackgroundOsteoarthritis (OA) is one of the most prevalent chronic diseases, leading to degeneration of joints, chronic pain, and disability in the elderly. Little is known about the role of immune-related genes (IRGs) and immune cells in OA.MethodHub IRGs of OA were identified by differential expression analysis and filtered by three machine learning strategies, including random forest (RF), least absolute shrinkage and selection operator (LASSO), and support vector machine (SVM). A diagnostic nomogram model was then constructed by using these hub IRGs, with receiver operating characteristic (ROC) curve, decision curve analysis (DCA), and clinical impact curve analysis (CICA) estimating its performance and clinical impact. Hierarchical clustering analysis was then conducted by setting the hub IRGs as input information. Differences in immune cell infiltration and activities of immune pathways were revealed between different immune subtypes.ResultFive hub IRGs of OA were identified, including TNFSF11, SCD1, PGF, EDNRB, and IL1R1. Of them, TNFSF11 and SCD1 contributed the most to the diagnostic nomogram model with area under the curve (AUC) values of 0.904 and 0.864, respectively. Two immune subtypes were characterized. The immune over-activated subtype showed excessively activated cellular immunity with a higher proportion of activated B cells and activated CD8 T cells. The two phenotypes were also seen in two validation cohorts.ConclusionThe present study comprehensively investigated the role of immune genes and immune cells in OA. Five hub IRGs and two immune subtypes were identified. These findings will provide novel insights into the diagnosis and treatment of OA.

The generalized Maxwell (GM) constitutive model has been widely applied to characterize the viscoelastic properties of asphalt mixtures. The parameters (Prony series) of the GM are usually obtained via interconversion between a dynamic modulus and relaxation modulus, and they are then input to a finite element model (FEM) as viscoelastic parameters. However, the dynamic modulus obtained with the common loading mode only provides the compressive and tensile properties of materials. Whether the compression or tensile modulus can represent the shear properties of materials related to flow rutting is still open to discussion. Therefore, this study introduced a novel method that integrates the Kriging model into the genetic algorithm as a surrogate model to determine the viscoelastic parameters of an asphalt mixture in rutting research. Firstly, a wheel tracking test (WTT) for AC-13 was conducted to clarify the flow rutting development mechanism. Secondly, two sets of the AC-13 viscoelastic parameters obtained through the optimization method and the dynamic modulus were used as inputs into the FEM simulation of the WTT to compare the simulation results. Finally, a sensitivity analysis of viscoelastic parameters was performed to improve the efficiency of parameter optimization. The results indicating the viscoelastic parameters obtained by this method could precisely characterize the development law of flow rutting in asphalt mixtures.

Water quality depends on its physicochemical and biological parameters. Changes in parameters such as pH, temperature, and essential and non-essential trace metals in water can render it unfit for human use. Moreover, the characteristics of the local environment, geological processes, geochemistry, and hydrological properties of water sources also affect water quality. Generally, groundwater is utilized for drinking purposes all over the globe. The surface is also utilized for human use and industrial purposes. There are several natural and anthropogenic activities responsible for the heavy metal contamination of water. Industrial sources, including coal washery, steel industry, food processing industry, plastic processing, metallic work, leather tanning, etc., are responsible for heavy metal contamination in water. Domestic and agricultural waste is also responsible for hazardous metallic contamination in water. Contaminated water with heavy metal ions like Cr (VI), Cd (II), Pb (II), As (V and III), Hg (II), Ni (II), and Cu (II) is responsible for several health issues in humans, like liver failure, kidney damage, gastric and skin cancer, mental disorders and harmful effects on the reproductive system. Hence, the evaluation of heavy metal contamination in water and its removal is needed. There are several physicochemical methods that are available for the removal of heavy metals from water, but these methods are expensive and generate large amounts of secondary pollutants. Biological methods are considered cost-effective and eco-friendly methods for the remediation of metallic contaminants from water. In this review, we focused on water contamination with toxic heavy metals and their toxicity and eco-friendly bioremediation approaches.

Objective: Osteoporosis is a common musculoskeletal disease. Fractures caused by osteoporosis place a huge burden on global healthcare. At present, the mechanism of metabolic-related etiological heterogeneity of osteoporosis has not been explored, and no research has been conducted to analyze the metabolic-related phenotype of osteoporosis. This study aimed to identify different types of osteoporosis metabolic correlates associated with underlying pathogenesis by machine learning.Methods: In this study, the gene expression profiles GSE56814 and GSE56815 of osteoporosis patients were downloaded from the GEO database, and unsupervised clustering analysis was used to identify osteoporosis metabolic gene subtypes and machine learning to screen osteoporosis metabolism-related characteristic genes. Meanwhile, multi-omics enrichment was performed using the online Proteomaps tool, and the results were validated using external datasets GSE35959 and GSE7429. Finally, the immune and stromal cell types of the signature genes were inferred by the xCell method.Results: Based on unsupervised cluster analysis, osteoporosis metabolic genotyping can be divided into three distinct subtypes: lipid and steroid metabolism subtypes, glycolysis-related subtypes, and polysaccharide subtypes. In addition, machine learning SVM identified 10 potentially metabolically related genes, GPR31, GATM, DDB2, ARMCX1, RPS6, BTBD3, ADAMTSL4, COQ6, B3GNT2, and CD9.Conclusion: Based on the clustering analysis of gene expression in patients with osteoporosis and machine learning, we identified different metabolism-related subtypes and characteristic genes of osteoporosis, which will help to provide new ideas for the metabolism-related pathogenesis of osteoporosis and provide a new direction for follow-up research.

Abstract Asphalt binder is the main material for road pavement and building construction. It is a complex mixture composed of a large number of hydrocarbons with different molecular weights. The study of asphalt binders and asphalt concretes from a molecular perspective is an important means to understand the intricate properties of asphalt. Molecular dynamics simulation is based on Newton’s law and predicts the microscopic performance of materials by calculating the intra- and intermolecular interactions. The asphalt binder can be divided into four components: saturates, aromatics, resins, and asphaltenes (SARA). A new molecular model of asphalt was proposed and verified in this study. Eight molecules selected from the literature were used to represent the four components of asphalt. The AMBER Cornell Extension Force Field was applied in this study to model building and the calculation of properties. The density of the asphalt model was calculated and compared with experimental results for validity verifications. The results show that the purposed model can be used to calculate the microscopic properties of the asphalt binder because the density of the model is close to the real value in the field. Besides, the proportions of different molecules in the model were adjusted to predict the relationship between the asphalt binder density and the hydrocarbon ratios and heteroatom contents of the molecular model. Moreover, the glass transition temperature of the asphalt binder model is predicted by the simulation of the heating process. The range of the glass transition temperature is determined by calculating the relationship between specific volume and temperature, and the calculated range is close to the experimental value.

Recognizing changes in the supply–demand relationship of Flood Regulation Service (FRS) is significant for the rational planning of urban patterns and the prevention of flooding risks in river basins. Most existing studies have explored its spatial pattern but pay less attention to its spatiotemporal changes. This study explored the dynamic changes in the supply–demand relationship of FRS in rapidly urbanizing basins. We proposed the Supply-Demand Ratio (SDR) method, which used the Soil Conservation Service Curve Number (SCS-CN) model and the flood vulnerability index to quantify the supply and demand of FRS, and then, we calculated the SDR. Additionally, we calculated the rate of change in SDR from 1990 to 2018 in the basin and its subbasins. The case study in the Baiyangdian Lake Basin showed that the SDR method can effectively measure the changes in the relationship between the supply and demand of FRS in the river basin. The SDR was consistent with the supply–demand bivariate mapping method, with Kappa coefficients of 0.610 and 0.835 in 1990 and in 2018, respectively. Furthermore, we found that from 1990 to 2018, the imbalance of FRS in supply and demand intensified. The SDR for the whole basin decreased by 0.088 from 1990 to 2018, with a change rate of −27.8%. Rapid urbanization is an important reason for intensification of the FRS supply–demand imbalance. The proportion of urban area is the most relevant factor for the supply–demand relationship of FRS, with a Pearson correlation coefficient of −0.845 (p

Abstract Ultra-dense networks (UDNs) have become an important architecture for the fifth generation (5G) networks. A large number of small base stations (SBSs) are deployed to provide high-speed and seamless connections for users in the network. However, the advantage of increasing the system capacity brought by the dense distribution of SBSs comes at the cost of severe inter-cell interference. Although the user-centric virtual cell method has been proposed to solve the interference problem, some challenges have been encountered in practical applications. For example, inter-cell interference still exists to a certain extent, and the cell load may be imbalance. Hence, under the virtual cell architecture, we propose a quality of service (QoS)-based joint user association and resource allocation scheme in UDNs. In order to mitigate the interference, balance cell load and improve the system throughput, a non-convex NP-hard problem is formulated. To effectively solve this problem, we decouple the formulated problem into three sub-problems: user association, physical resource block (PRB) allocation and power allocation. First, we consider the QoS requirements of user equipment (UE) and perform user association based on the PRB estimation method. Then, based on the overlapped virtual cells constructed, we propose a graph-based PRB allocation scheme for reducing virtual inter-cell interference. Moreover, we solve power allocation sub-problem by using the difference of concave (DC) programing method. The simulation results show that our proposed scheme is superior to other schemes in terms of user rates, cell load and system throughput.

In this article, we consider a single unmanned aerial vehicle (UAV)-assisted heterogeneous network in a disaster area, which includes a UAV, ground cellular users, and ground sensor users. The cellular data and sensing data are transmitted to UAVs by cellular users and sensor users, due to the outage of the ground wireless network caused by the disaster. In this scenario, we aim to minimize the energy consumption of all the users, to extend their communication time and facilitate rescue. At the same time, cellular users and sensor users have different rate requirements, hence the quality of service (QoS) of the users should be guaranteed. To solve these challenges, we propose an energy-effective relay selection and resource-allocation algorithm. First, to solve the problem of insufficient coverage of the single UAV network, we propose to perform multi-hop transmission for the users outside the UAV’s coverage by selecting suitable relays in an energy-effective manner. Second, for the cellular users and sensor users inside the coverage of the UAV but with different QoS requirements, we design a non-orthogonal multiple access (NOMA)-based transmission scheme to improve spectrum efficiency. Deep reinforcement learning is exploited to dynamically adjust the power level and allocated sub-bands for inside users to reduce energy consumption and improve QoS satisfaction. The simulation results show that the proposed NOMA transmission scheme can achieve 9–17% and 15–32% performance gain on the reduction of transmit power and the improvement of QoS satisfaction, respectively, compared with state-of-the-art NOMA transmission schemes and orthogonal multiple access scheme.

Background Cognitive impairment after anaesthesia and surgery is a recognised consequence. This often leads to poor health outcomes and increases healthcare resource utilisation and associated costs, especially in elderly people. However, thus far, there have not been any effective therapies for managing postoperative cognitive dysfunction (POCD). Furthermore, research on the association of multimodal warming with POCD and the clinical outcomes in older patients after gynaecological surgery has not been rigorous. For these reasons, our investigation aims to evaluate whether perioperative multimodal warming would reduce the incidence of POCD and improve prognosis in elderly patients with gynaecological cancer.Methods and analysis This is a single-centre, prospective, single-blinded randomised controlled trial. One hundred and fifty patients for gynaecological cancer surgery and 16 non-surgical controls aged 65 years or older will be studied in this trial. A series of neuropsychological tests will be completed to evaluate cognitive function in surgery patients before, at day 7 and 3 months after gynaecological cancer surgery. In addition, POCD and cognitive decline will be assessed using the reliable change index using the control group’s results. The primary outcome is the prevalence of POCD in elderly gynaecological cancer surgery patients and association between perioperative multimodal warming and POCD.Ethics and dissemination The protocol for this prospective observational study was approved by the ethics committee of the West China Second University Hospital, Sichuan University (NO. KX215). Recruitment will commence in April 2021 and continue to April 2022. The findings of this trial will be disseminated in peer-reviewed journals and scientific meetings.Trial registration number ChiCTR2100041663.

Abstract Flexoelectricity is an electromechanical coupling effect in which electric polarization is generated by a strain gradient. In this investigation, a potassium sodium niobite/poly(vinylidene fluoride‐trifluoroethylene) (KNN/PVDF‐TrFE)‐based nanocomposite is fabricated, and the flexoelectric effect is used to enhance the photovoltaic current (Ipv) in the nanocomposite. It is found that both a pyroelectric current and photovoltaic current can be generated simultaneously in a light illumination process. However, the photovoltaic current (Ipv) in this process contributes ≈85% of the total current. When assessing the effect of flexoelectricity with a curvature of 1/20, the Ipv of the curved KNN/PVDF‐TrFE (20%) (K/P‐20) composite increased by ≈13.9% compared to that of the flat K/P‐20 nanocomposite. Similarly, at a curvature of 1/20, the Ipv of the K/P‐20 nanocomposite is 71.6% higher than that of the PVDF‐TrFE film. However, the photovoltaic effect induced by flexoelectricity is much higher than the increased polarization from flexoelectricity, so this effect is called as the flexophotovoltaic effect. Furthermore, the calculated energy conversion efficiency of the K/P‐20 film is 0.017%, which is comparable to the previous research result. This investigation shows great promise for PVDF‐based nanocomposites in ferroelectric memory device applications.

The development of technology has strongly affected regional urbanization. With development of mobile communication technology, intelligent devices have become increasingly widely used in people’s lives. The application of big data in urban computing is multidimensional; it has been involved in different fields, such as urban planning, network optimization, intelligent transportation, energy consumption and so on. Data analysis becomes particularly important for wireless networks. In this paper, a method for analyzing cellular traffic data was proposed. Firstly, a method to extract trend components, periodic components and essential components from complex traffic time series was proposed. Secondly, we introduced causality data mining. Different from traditional time series causality analysis, the depth of causal mining was increased. We conducted causality verification on different components of time series and the results showed that the causal relationship between base stations is different in trend component, periodic component and essential component in urban wireless network. This is crucial for urban planning and network management. Thirdly, DIC-ST: a spatial temporal time series prediction based on decomposition and integration system with causal structure learning was proposed by combining GCN. Final results showed that the proposed method significantly improves the accuracy of cellular traffic prediction. At the same time, this method can play a crucial role for urban computing in network management, intelligent transportation, base station siting and energy consumption when combined with remote sensing map information.

Energy Harvesting (EH) is a promising paradigm for 5G heterogeneous communication. EH-enabled Device-to-Device (D2D) communication can assist devices in overcoming the disadvantage of limited battery capacity and improving the Energy Efficiency (EE) by performing EH from ambient wireless signals. Although numerous research works have been conducted on EH-based D2D communication scenarios, the feature of EH-based D2D communication underlying Air-to-Ground (A2G) millimeter-Wave (mmWave) networks has not been fully studied. In this paper, we considered a scenario where multiple Unmanned Aerial Vehicles (UAVs) are deployed to provide energy for D2D Users (DUs) and data transmission for Cellular Users (CUs). We aimed to improve the network EE of EH-enabled D2D communications while reducing the time complexity of beam alignment for mmWave-enabled D2D Users (DUs). We considered a scenario where multiple EH-enabled DUs and CUs coexist, sharing the full mmWave frequency band and adopting high-directive beams for transmitting. To improve the network EE, we propose a joint beamwidth selection, power control, and EH time ratio optimization algorithm for DUs based on alternating optimization. We iteratively optimized one of the three variables, fixing the other two. During each iteration, we first used a game-theoretic approach to adjust the beamwidths of DUs to achieve the sub-optimal EE. Then, the problem with regard to power optimization was solved by the Dinkelbach method and Successive Convex Approximation (SCA). Finally, we performed the optimization of the EH time ratio using linear fractional programming to further increase the EE. By performing extensive simulation experiments, we validated the convergence and effectiveness of our algorithm. The results showed that our proposed algorithm outperformed the fixed beamwidth and fixed power strategy and could closely approach the performance of exhaustive search, particle swarm optimization, and the genetic algorithm, but with a much reduced time complexity.

The prediction of additional stresses in ballasted track due to subgrade deformation is the main objective of the present paper. In this context, a 2D finite element model of ballasted railway track was built using the ANSYS Workbench program. Based on this model, an investigation of stresses and deformation values of track elements was conducted in three cases with different contact types. It was found that the case introducing the status of a new track, which has frictional contacts between sleepers and ballast with bonded contacts between other elements, has lower stresses in most of the track elements. Moreover, this case was applied for studying the effect of the settlement on track elements. It was found that stresses increased with increasing the settlement value. The average percentages of increased stresses are 4.18%, 5.85%, and 7.21% in railhead, tie plate, and sleeper, respectively, due to a 1 mm increase in the settlement. Finally, a second-degree polynomial equation was derived to predict the additional stresses in each element due to track settlement. It is expected that this study would help to decrease the maintenance costs and extend the service life of the track elements by predicting the additional stresses in them.

Inefficiency in urban land use is one of the problems caused by rapid urbanization. The UN Sustainable Development Goals (SDGs) indicator 11.3.1 is designed to test urban land use efficiency. This study employed geospatial and statistical data to compute land use efficiencies from 1990 to 2015 with five 5-year and ten 15-year intervals in Wukang, center of Deqing County, China. A flowchart was designed to extract the built-up lands from multiple data sources. The produced built-up lands were demonstrated to provide good accuracy by constructing an error matrix between the extracted and manually interpreted built-up lands as classified and reference images, respectively. By using the model provided by UN metadata to calculate SDG 11.3.1, the land use efficiencies from 1990 to 2015 were identified in Wukang. Our results indicate that the land use efficiency in Deqing County center is lower than the average of cities around the world, primarily because our in-situ study focused on a county center with larger rural regions than urban areas. Over the long term, urban land use becomes denser as the population grows, which will have a positive impact on the sustainability of urban development. This work is helpful for the local government to balance urban land consumption and population growth.

This paper studies beam allocation and power optimization scheme to decrease the hardware cost and downlink power consumption of a multiuser millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) system. Our target is to improve energy efficiency (EE) and decrease power consumption without obvious system performance loss. To this end, we propose a beam allocation and power optimization scheme. First, the problem of beam allocation and power optimization is formulated as a multivariate mixed-integer non-linear programming problem. Second, due to the non-convexity of this problem, we decompose it into two sub-problems which are beam allocation and power optimization. Finally, the beam allocation problem is solved by using a convex optimization technique. We solve the power optimization problem in two steps. First, the non-convex problem is converted into a convex problem by using a quadratic transformation scheme. The second step implements Lagrange dual and sub-gradient methods to solve the optimization problem. Performance analysis and simulation results show that the proposed algorithm performs almost identical to the exhaustive search (ES) method, while the greedy beam allocation and suboptimal beam allocation methods are far from the ES. Furthermore, experiment results demonstrated that our proposed algorithm outperforms the compared the greedy beam allocation method and the suboptimal beam allocation scheme in terms of average service ratio.

Unmanned Aerial Vehicle (UAV) has been widely used in various applications of wireless network. A system of UAVs has the function of collecting data, offloading traffic for ground Base Stations (BSs) and illuminating coverage holes. However, inter-UAV interference is easily introduced because of the huge number of LoS paths in the air-to-ground channel. In this paper, we propose an interference management framework for UAV-assisted networks, consisting of two main modules: power control and UAV clustering. The power control is executed first to adjust the power levels of UAVs. We model the problem of power control for UAV networks as a non-cooperative game which is proved to be an exact potential game and the Nash equilibrium is reached. Next, to further improve system user rate, coordinated multi-point (CoMP) technique is implemented. The cooperative UAV sets are established to serve users and thus transforming the interfering links into useful links. Affinity propagation is applied to build clusters of UAVs based on the interference strength. Simulation results show that the proposed algorithm integrating power control with CoMP can effectively reduce the interference and improve system sum-rate, compared to Non-CoMP scenario. The law of cluster formation is also obtained where the average cluster size and the number of clusters are affected by inter-UAV distance.

Current research on the inhibition of Microcystis aeruginosa growth is primarily focused on algae-lysing bacteria, and few studies have investigated the inhibitory mechanisms by which fungi affect it at the molecular level. A comparative analysis of the effects of Phanerochaete chrysosporium on the expression of the algal cell antioxidant protease synthesis gene prx, the biological macromolecule damage and repair genes recA, grpE, and fabZ, and the photosynthesis system-related genes psaB, psbD1 and rbcL, as well as genes for algal toxin synthesis mcyB, were performed to elucidate the molecular mechanism of Phanerochaete chrysosporium against Microcystis aeruginosa cells. RT-qPCR technology was used to study the molecular mechanism of algal cell inhibition by Phanerochaete chrysosporium liquid containing metabolites of Phanerochaete chrysosporium, Phanerochaete chrysosporium supernatant and Phanerochaete chrysosporium inactivated via high temperature sterilization at the gene expression level. Compared with the control, the chlorophyll-a contents dropped, and the recA, grpE, fabZ, and prx increased, but the psaB, psbD1, rbcL and mcyB showed that they were significantly reduced, which indicated that Phanerochaete chrysosporium can not only effectively destroy algal cells, but they may also reduce the expression of the Microcystis aeruginosa toxin gene and significantly block the metabolic system underlying the growth of algal cells and the synthesis of microcystins.