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Advancements in assisted driving technologies are expected to enable future passengers to use a wide range of multimedia applications in electric vehicles (EVs). To address the bandwidth demands for high-resolution and immersive videos during peak traffic, this study introduces a bandwidth-management algorithm to support differentiated streaming services in heterogeneous vehicle-to-everything (V2X) networks. By leveraging cellular 6G base stations, along with Cell-Free (CF) Massive Multi-Input Multi-Output (mMIMO) Wi-Fi 7 access points, the algorithm aims to provide a high-coverage, high-speed, and low-interference V2X network environment. Additionally, Li-Fi technology is employed to supply extra bandwidth to vehicles with limited connectivity via V2V communication. Importantly, the study addresses the urgency and prioritization of different applications to ensure the smooth execution of emergency applications and introduces a pre-downloading mechanism specifically for non-real-time applications. Through simulations, the algorithm’s effectiveness in meeting EV users’ bandwidth needs for various multimedia streaming applications is demonstrated. During peak-bandwidth-demand periods, users experienced an average increase in bandwidth of 47%. Furthermore, bandwidth utilization across the V2X landscape is significantly improved.

It is expected that interconnected networks of autonomous vehicles, especially during peak traffic, will face congestion challenges. Moreover, the existing literature lacks discussions on integrating next-generation wireless communication technologies into connected vehicular networks. Hence, this paper introduces a tailored bandwidth management algorithm for streaming applications of autonomous vehicle passengers. It leverages cutting-edge 6G wireless technology to create a network with high-speed transmission and broad coverage, ensuring smooth streaming application performance. The key features of bandwidth allocation for diverse streaming applications in this work include bandwidth relay and pre-loading of video clips assisted by vehicle-to-vehicle communication. Through simulations, this research effectively showcases the algorithm’s ability to fulfill the bandwidth needs of diverse streaming applications for autonomous vehicle passengers. Specifically, during periods of peak user bandwidth demand, it notably increases the bandwidth accessible for streaming applications. On average, users experience a substantial 55% improvement in the bandwidth they can access. This validation affirms the viability and promise of the proposed approach in efficiently managing the intricate complexities of bandwidth allocation issues for streaming services within the connected autonomous vehicular networks.

Abstract Background Accumulating studies indicated that dysregulated long non-coding RNA human histocompatibility leukocyte antigen (HLA) Complex P5 (HCP5) may functions as an potential prognostic predictor in multiple cancers. This meta-analysis was performed to systematically collect studies and conduct an evidence-based evaluation of the prognostic role of HCP5 in malignancies. Methods Four databases (PubMed, Web of Science, Embase and Cochrane library) were comprehensively retrieved from their initiation date to November 9, 2021. Hazard ratio (HR) or odds ratio (OR) with 95% confidence interval (CI) were used to assess the associations between the expression level of HCP5 and prognosis or clinical characteristics. Moreover, results were validated by Gene Expression Profiling Interactive Analysis 2 (GEPIA2) and the National Genomics Data Center (NGDC). Subsequently, the molecular mechanism of HCP5 was predicted based on MEM and StarBase databases. The study protocol was registered at PROSPERO (ID: CRD42021274208). Results 9 studies, containing 641 patients, were included in this meta-analysis. Our results revealed that HCP5 overexpression was associated with poor overall survival (OS), tumor type, histological differentiation, and lymph node metastasis in most cancers, but was not associated with age, gender and tumor size; down-regulation of HCP5 was associated with worse OS, advanced tumor stage, positive distal metastasis and lymph node metastasis in skin cutaneous melanoma (SKCM). HCP5 was significantly up-regulated in four cancers and down-regulated in SKCM, which was validated by the GEPIA2 cohort. HCP5 expression in various types of cancer was also verified in NGDC. Further functional prediction revealed that HCP5 may participate in some cancer-related pathways. Conclusion There is a significantly association between dysregulation of HCP5 and both prognosis and clinicopathological features in various cancers. HCP5 may be functions as a novel potential prognostic biomarker and therapeutic target in multiple human cancers.

The popularity of video services such as video call or video on-demand has made it impossible for people to live without them in their daily lives. It can be anticipated that the explosive growth of vehicular communication owing to the widespread use of in-vehicle video infotainment applications in the future will result in increasing fragmentation and congestion of the wireless transmission spectrum. Accordingly, effective bandwidth management algorithms are demanded to achieve efficient communication and stable scalability in next-generation vehicular networks. To the best of our current knowledge, a noticeable gap remains in the existing literature regarding the application of the latest advancements in network communication technologies. Specifically, this gap is evident in the lack of exploration regarding how cutting-edge technologies can be effectively employed to optimize bandwidth allocation, especially in the realm of video service applications within the forthcoming vehicular networks. In light of this void, this paper presents a seamless integration of cutting-edge 6G communication technologies, such as terahertz (THz) and visible light communication (VLC), with the existing 5G millimeter-wave and sub-6 GHz base stations. This integration facilitates the creation of a network environment characterized by high transmission rates and extensive coverage. Our primary aim is to ensure the uninterrupted playback of real-time video applications for vehicle users. These video applications encompass video conferencing, live video, and on-demand video services. The outcomes of our simulations convincingly indicate that the proposed strategy adeptly addresses the challenge of bandwidth competition among vehicle users. Moreover, it notably boosts the efficient utilization of bandwidth from less crowded base stations, optimizes the fulfillment of bandwidth prerequisites for various video applications, and elevates the overall video quality experienced by users. Consequently, our findings serve as a successful validation of the practicality and effectiveness of the proposed methodology.

In addition to traditional battery exchange services and stationary charging stations, researchers have proposed wireless charging technology, such as decentralized laser charging or drone-to-drone charging in flight, to provide power to drones with insufficient battery electricity. However, the charging methods presented in the literature will inevitably cause drones to wait in line for charging during peak hours and disrupt their scheduled trips when the number of drones grows rapidly in the future. To the best of our knowledge, there have been no integrated solutions for drone flight path and charging planning to alleviate charging congestion, taking into account the different mission characteristics of drones and the charging cost considerations of drone operators. Accordingly, this paper provides adaptive charging options to help drone operators to solve the above-mentioned problems. Drones on ordinary missions can use conventional battery swap services, wired charging stations, or electromagnetic wireless charging stations to recharge their batteries as usual, whereas drones on time-critical missions can choose drone-to-drone wireless charging or decentralized laser charging deployed along the fight paths to charge the batteries of drones in flight. Notably, since fixed-wing drones have larger wing areas to install solar panels, they can also use solar energy to charge during flight if the weather is clear. The simulation results exhibited that the proposed work reduced the power load of the power grid during peak hours, met the charging needs of each individual drone during flight, and cut down the charging costs of drone operators. As a result, an all-win situation for drone operators, drone customers, and power grid operators was achieved.

Traffic congestion in urban areas has become a critical problem that municipal governments cannot overlook. Meanwhile, mixed traffic systems containing both autonomous and human-driven electric vehicles ramp up the challenge for traffic management in urban areas. Although numerous researchers have proposed traffic control heuristics to alleviate traffic congestion problems in the recent literature, scant research has addressed the joint problems of route and charging strategies for electric vehicles along with urban traffic congestion prevention. Accordingly, this work tackles the complex task of traffic management in urban areas during peak periods by using practical congestion prevention strategies that consider the characteristics of mixed traffic flows and the charging demands of electric vehicle users. Notably, we apply support vector regressions to compute the charging time at each charging point and the traverse time of an electric vehicle at each road segment/intersection, based on historical traffic data. The simulation results reveal that the proposed algorithms are feasible because they can avoid possible occurrences of traffic congestion during rush hours and provide the routes and charging options that are chosen by electric vehicle users.

With the rapid development of the emerging technologies and significant cost reduction of the deployment for solar energy and wind power, the replacement of traditional power generation by renewable energy becomes feasible in the future. However, different from currently deployed centralized power sources, renewables are categorized as one kind of intermittent energy sources, and the scale of renewables is small and scattered. In the recent literature, the architecture of virtual power plant was proposed to replace the current smart grid in the future. However, the energy sharing concept and the uncertainties of intermittent energy sources will cause the short-term energy management for the virtual power plant much more complicated than the current centralized control energy management for traditional power generation system. We thus propose a hierarchical day-ahead power scheduling system for virtual power plant in this work to tackle the complex short-term energy management problems. We first collect electricity consumption data from smart appliances used in households and predict power-generating capacity of renewable energy sources at the prosumer level. Then, the proposed hierarchical power scheduling system is employed to schedule the usage of electricity for the customers by considering the efficiency of the use of distributed renewables. Notably, charging management of a moving electric vehicle is also considered in the proposed power scheduling mechanism. In addition, a real-time power tracking mechanism is presented to deal with the forecast errors of volatile renewable power generation, electricity load, and moving electric vehicle charging, and the maximal usage of renewables and reduction of the burden on community virtual power plants during time period of peak load can be achieved accordingly. The experimental results show that the proposed day-ahead power scheduling system can mitigate the dependency on traditional power generation effectively, and balance peak and off-peak period load of electricity market.

With the growth up of internet in mobile commerce, researchers have reproduced various mobile applications that vary from entertainment and commercial services to diagnostic and safety tools. Mobility management has widely been recognized as one of the most challenging problems for seamless access to wireless networks. In this paper, a novel link enhancement mechanism is proposed to deal with mobility management problem in vehicular ad hoc networks. Two machine learning techniques, namely, particle swarm optimization and fuzzy logic systems, are incorporated into the proposed schemes to enhance the accuracy of prediction of link break and congestion occurrence. The experimental results verify the effectiveness and feasibility of the proposed schemes.

Objective: To analyze the potential targets and mechanism of Xingxiao Pill for the treatment of lung cancer based on network pharmacology and molecular docking, providing reference for the research and clinical development of Xingxiao Pill. Method: The main chemical constituents and their targets of Xingxiao Pill was obtained through BATMAN-TCM database. Effective compounds were screened based on Swiss ADME and their targets were predicted by Swiss Target Prediction. Main lung cancer targets were obtained and integrated through GeneCards, OMIM(OMIM-Gene-Map-Retrieval), TTD and DRUGBANK databases. The intersection targets of Xingxiao Pill for lung cancer were obtained by Venn. The String platform was used to obtain the intersection targets. The core targets of Xingxiao Pill for lung cancer were screened by BisoGenet3.0. The Metasacpe database was used to analyze the biological processes and pathways involved in intersection targets. Cytoscape3.7.2 software was used to construct the "active ingredients of Xingxiao Pill-targets-pathways" network. Finally, Autodock was used for molecular docking verification. Results: The core active components of Xingxiao Pill for the treatment of lung cancer were morin, testosterone, 17-Beta-Estradiol, alpha Estradiol, muscone, commiferin, commisterone, octyl acetate, etc. The primary pathways mainly included PI3K/Akt, MTOR, MAPK signaling pathway and MicroRNAs in cancer, etc. It has been proved that the combination of testosterone and estradiol with AKT1, SIRT7 and MDM2 was stable and could be used as reference. Conclusion: This study preliminarily revealed the mechanism of Xingxiao Pill on treating lung cancer with multiple components, targets and pathways, providing some ideas and references for clinical application and basic research in the future. [ABSTRACT FROM AUTHOR]

Objective: To study the regulatory effect of cryoablation on MAPK/ERK signaling pathway in mice with lung adenocarcinoma. Methods: Lewis mouse lung adenocarcinoma cell line was used to establish subcutaneous transplanted tumor model in C57BL / 6 mice. Ten mice were randomly divided into two groups: sham operation group and cryoablation group, with 5 mice in each group. The cryoablation group was treated with double circulation-rewarming ablation, and the sham operation group was treated with incision and suture at the transplanted tumor. The tumor tissues were taken 14 days after operation. Detect the effect of cryoablation on MAPK/ERK pathway related proteins by Western blot, such as KRAS, RAF1, MEK1, ERK1/2, P-RAF1, P-MEK1, P-ERK1/2. The expression of KRAS gene was further verified by qRt-PCR. Results: Compared with the sham operation group, the phosphorylated proteins P-RAF1, P-MEK1 and P-ERK1/2 in tumor tissue after cryoablation were decreased (P< 0.05), and the key molecule KRAS in MAPK/ERK pathway was decreased in protein and gene expression (P< 0.05). Conclusion: Cryoablation can negatively regulate MAPK / ERK signaling pathway by down-regulating KRas expression. [ABSTRACT FROM AUTHOR]

Objective: To explore the relationship between platelet/lymphocyte ratio (PLR) and neutrophil/lymphocyte ratio (NLR) and prognosis of lung cancer patients before cold ablation. Methods: Retrospectively analyze the case data of primary lung cancer patients who underwent cryoablation of lung argon helium knife in our hospital from January 2012 to March 2018. PLR and NLR were calculated based on the blood routine data of the patients within 1 week before the operation. value. The patients were followed up. The deadline for follow-up was June 2021. The survival curve (ROC curve) was drawn according to the patient's survival prognosis, and the Youden index was calculated to determine the optimal cutoff value of PLR and NLR, and grouped based on this analysis. The correlation between pre-PLR level and NLR level with clinicopathological characteristics and prognostic survival time of patients with primary lung cancer. Results: The best cut-off point was calculated according to the ROC curve and divided into high PLR group (PLR=155.72, n=127) and low PLR group (<155.72, n=87); high NLR group (=2.91, n=120) and Low NLR group (<2.91, n=94); preoperative high PLR, high NLR group pT, pN, pM and TNM staging compared with low value group, the difference was statistically significant (P<0.05); Cox multivariate Analysis shows that high PLR, high NLR, high pT staging, high pM staging, and high TNM staging are independent risk factors affecting postoperative survival; the postoperative survival rate of the high-level PLRNLR group was significantly lower than that of the other groups, and the difference was statistically significant. significance. Conclusion: The significant increase of NLR and PLT before operation in patients with primary lung cancer is associated with poor prognosis; the combination of the two has potential clinical application value in assessing the survival of patients after surgery. [ABSTRACT FROM AUTHOR]