Search

DongJu Long,1 Kai Chen,1 YaXi Li,1 PeiYao He,1 XinNing Li,1,2 XiuNan Qin,1 YaPing Wang,1,2 YanYing Xiao1,2 1Department of Anesthesiology and Pain Management, The Second Xiangya Hospital, Central South University, Changsha, Hunan, People’s Republic of China; 2Department of Pain Management, Clinical Research Center for Pain Medicine in Hunan Province, Changsha, Hunan, People’s Republic of ChinaCorrespondence: YanYing Xiao, Department of Anesthesiology and Pain Management, The Second Xiangya Hospital, Central South University, Changsha, Hunan, People’s Republic of China, Tel +86-13467609491, Fax +86-731-85292115, Email xiaoyanying192@csu.edu.cnBackground: Trigeminal neuralgia usually presents as incapacitating facial pain. Percutaneous balloon compression (PBC) is frequently utilized to manage this ailment. Trigeminocardiac reflex (TCR) commonly presents with sudden severe bradycardia or even asystole, alongside a sudden increase in blood pressure during this surgical procedure. Notably, remimazolam has been reported to maintain higher heart rate (HR) levels during anesthesia than propofol. Thus, this study aims to assess the impact of remimazolam anesthesia versus propofol on TCR occurrence during this procedure.Methods: This randomized controlled trial involved patients with trigeminal neuralgia scheduled for elective PBC. Patients were randomly assigned to receive either remimazolam or propofol for anesthesia. The primary outcome was the incidence of TCR, a potential complication during the procedure. Secondary outcomes included the occurrence of severe TCR, usage of atropine, HR at the time of foramen ovale puncture (T4), HR at the time of trigeminal ganglion compression (T5), and any adverse events.Results: A total of 80 patients were included in the study, with 40 patients in each group. The incidence of TCR was significantly lower in the remimazolam group compared to the propofol group (30.0% vs 82.5%; risk difference − 52.5%, 95% CI − 67.3% to − 18.6%; P < 0.001). The remimazolam group also showed a lower incidence of severe TCR (7.5% vs 45.0%) and significantly lower usage of atropine compared to the propofol group (P < 0.001). Furthermore, HR at T4 and T5 were higher in the remimazolam than in the propofol group (P < 0.001). There was no significant difference in the incidence of adverse events between the two groups.Conclusion: In PBC surgery for trigeminal neuralgia, remimazolam-based intravenous anesthesia showed a higher HR and a lower incidence of TCR than propofol without any increased adverse events.Keywords: remimazolam, propofol, trigeminal neuralgia, balloon occlusion, methods, reflex, trigeminocardiac, hemodynamics

Lei Tan,1,2,&ast; Han She,1,2,&ast; Yi Wang,1,2,&ast; Yuanlin Du,1 Jun Zhang,1 Yunxia Du,1 Yinyu Wu,1 Wei Chen,1 Bingqiang Huang,1 Duanyang Long,1 Xiaoyong Peng,2 Qinghui Li,2 Qingxiang Mao,1 Tao Li,2 Yi Hu1 1Department of Anesthesiology, Daping Hospital, Army Medical University, Chongqing, 400042, People’s Republic of China; 2State Key Laboratory of Trauma, Burns and Combined Injury, Shock and Transfusion Department, Daping Hospital, Army Medical University, Chongqing, 400042, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Tao Li; Yi Hu, Email lt200132@163.com; huyi921@sina.comBackground: Hemorrhagic shock was a leading cause of death worldwide, with myocardial injury being a primary affected organ. As commonly used solutions in fluid resuscitation, acetated Ringer’s (AR) and Lactate Ringer’s solution (LR) were far from perfect for their adverse reactions such as lactic acidosis and electrolyte imbalances. In previous studies, TPP@PAMAM-MR (TPP-MR), a novel nanocrystal resuscitation fluid has been found to protect against myocardial injury in septic rats. However, its role in myocardial injury in rats with hemorrhagic shock and underlying mechanism is unclear.Methods: The hemorrhagic shock rats and hypoxia-treated cardiomyocytes (H9C2) were utilized to investigate the impact of TPP-MR on cardiac function, mitochondrial function, and lipid peroxidation. The expressions of ferritin-related proteins glutathione peroxidase 4 (GPX4), Acyl CoA Synthase Long Chain Family Member 4 (ACSL4), and Cyclooxygenase-2(COX2) were analyzed through Western blotting to explore the mechanism of TPP-MR on hemorrhagic myocardial injury.Results: TPP-MR, a novel nanocrystalline resuscitation fluid, was synthesized using TPP@PAMAM@MA as a substitute for L-malic acid. We found that TPP-MR resuscitation significantly reduced myocardial injury reflected by enhancing cardiac output, elevating mean arterial pressure (MAP), and improving perfusion. Moreover, TPP-MR substantially prolonged hemorrhagic shock rats’ survival time and survival rate. Further investigations indicated that TPP-MR improved the mitochondrial function of myocardial cells, mitigated the production of oxidative stress agents (ROS) and increased the glutathione (GSH) content. Additionally, TPP-MR inhibited the expression of the ferroptosis-associated GPX4 protein, ACSL4 and COX2, thereby enhancing the antioxidant capacity.Conclusion: The results showed that TPP-MR had a protective effect on myocardial injury in rats with hemorrhagic shock, and its mechanism might be related to improving the mitochondrial function of myocardial cells and inhibiting the process of ferroptosis.Keywords: myocardial injury, hemorrhagic shock, mitochondria, ferroptosis

In order to overcome spectrum scarcity and provide higher data rates, the sixth-generation (6G) wireless communication network is expected to perform data transmission using terahertz (THz) frequencies. However, the effective implementation of these communication systems is hampered by severe levels of signal degradation to which the THz bandwidth is subject to. Recent improvements and advancements in the fabrication process of electromagnetic (EM) metamaterials have made reconfigurable intelligent surfaces (RIS) a very promising solution to address these THz-related attenuation issues. Additionally, the adoption of non-orthogonal multiple access (NOMA) transmissions represents an effective way to improve spectrum efficiency for 6G networks. In this paper, we investigate the problem of downlink aggregated sum-rate maximisation for a multiple-input multiple-output (MIMO) system assisted by a RIS panel in performing NOMA transmission within the THz bandwidth. More specifically, we propose an optimization algorithm that jointly optimizes the transmitting power at the access point (AP) and the phase-shift coefficients for the RIS elements iteratively. Through simulation results, we demonstrate that the proposed method outperforms conventional benchmark schemes in terms of achieved aggregated throughput.

Qingzhu Gao,1 Liangmou Gao,1 Dengjie Long,2 Yuege Wang3 1School of Business Administration, Dongbei University of Finance and Economics, Dalian, Liaoning, People’s Republic of China; 2Marxism College, Party School of Chongqing Committee of C.P.C, Chongqing, People’s Republic of China; 3Faculty of Agribusiness and Commerce, Lincoln University, Lincoln, New ZealandCorrespondence: Dengjie Long, Marxism College, Party School of Chongqing Committee of C.P.C, 160 Yuzhou Road, Jiulongpo District, Chongqing, People’s Republic of China, Tel +86 13036373662, Email cqlongdengjie@163.comPurpose: During recent years, there has been a growing interest in CSR across disciplines. Various scholars document that Chief Executive Officer (CEO) narcissism is an important factor that should not be overlooked when analyzing CSR. Research on the relationship between CEO narcissism and CSR has treated CSR as a whole construct. However, little attention has been paid to its effect on different dimensions of CSR, especially the same psychological trait may have effects on charitable donations and employee welfare. The purpose of the study is to explore the relationship between CEO narcissism and charitable donations and employee welfare, while taking into account the moderating role of the legal environment.Methods: This study used the video survey method to measure CEO narcissism, the video information was obtained from Baidu.com and hao.360.com search engines. Other data were collected from Chinese Stock Market Research (CSMAR) database. We used OLS regression for data analysis and also used Tobit regression model to check the robustness of the estimation results. Meanwhile, all analyses will be performed with Stata 16.0 software.Results: Empirical analysis reveals that CEO narcissism has a positive and significant impact on charitable donations and has a negative and significant impact on employee welfare. Moreover, the legal environment will reduce the effect of CEO narcissism on charitable donations and employee welfare, indicating that a stronger legal environment could attenuate the effect of CEO personality traits, especially narcissism on charity donations and employee welfare.Conclusion: This study contributes to the behavioral finance theory and stakeholder theory to better understand the relationship between CEO narcissism and charitable donations and employee welfare. Meanwhile, this study is one of the few studies to investigate the patterns of CSR activities in China, an important emerging economy.Keywords: CSR, CEO, legal environment, charitable donation, employee welfare, personality traits

Abstract Numerous ports worldwide are adopting automation to boost productivity and modernize their operations. At this point, smart ports become a more important paradigm for handling increasing cargo volumes and increasing operational efficiency. In fact, as ports become more congested and cargo volumes increase, the need for accurate navigation through seaports is more pronounced to avoid collisions and the resulting consequences. To this end, digital twin (DT) technology in the fifth-generation (5G) networks and drone-assisted data collection can be combined to provide precise ship maneuvering. In this paper, we propose a DT model using drone-assisted data collection architecture, called TwinPort, to offer a comprehensive port management system for smart seaports. We also present a recommendation engine to ensure accurate ship navigation within a smart port during the docking process. The experimental results reveal that our solution improves the trajectory performance by approaching the desired shortest path. Moreover, our solution supports significantly reducing financial costs and protecting the environment by reducing fuel consumption.

Rapid growth in urbanization and industrialization leads to an increase in air pollution and poor air quality. Because of its adverse effects on the natural environment and human health, it’s been declared a “silent public health emergency”. To deal with this global challenge, accurate prediction of air pollution is important for stakeholders to take required actions. In recent years, deep learning-based forecasting models show promise for more effective and efficient forecasting of air quality than other approaches. In this study, we made a comparative analysis of various deep learning-based single-step forecasting models such as long short term memory (LSTM), gated recurrent unit (GRU), and a statistical model to predict five air pollutants namely Nitrogen Dioxide (NO2), Ozone (O3), Sulphur Dioxide (SO2), and Particulate Matter (PM2.5, and PM10). For empirical evaluation, we used a publicly available dataset collected in Northern Ireland, using an air quality monitoring station situated in Belfast city centre. It measures the concentration of air pollutants. The performance of forecasting models is evaluated based on three performance metrics: (a) root mean square error (RMSE), (b) mean absolute error (MAE) and (c) R-squared ( $\text{R}^{2}$ ). The result shows that deep learning models consistently achieved the least RMSE compared to the statistical models with a value of 0.59. In addition, the deep learning model is also found to have the highest $\text{R}^{2}$ score of 0.856.

Dan Long,1 Ting Yang,1 Jie Chen,1 Ying Dai,1 Li Chen,1 Feiyong Jia,2 Lijie Wu,3 Yan Hao,4 Ling Li,5 Jie Zhang,6 Xiaoyan Ke,7 Mingji Yi,8 Qi Hong,9 Jinjin Chen,10 Shuanfeng Fang,11 Yichao Wang,12 Qi Wang,13 Chunhua Jin,14 Tingyu Li1 1Chongqing Key Laboratory of Child Nutrition and Health, Ministry of Education Key Laboratory of Child Development and Disorders, National Clinical Research Center of Child Health and Disorders, Children’s Hospital of Chongqing Medical University, Chongqing, People’s Republic of China; 2Department of Developmental and Behavioral Pediatrics, The First Hospital of Jilin University, Changchun, People’s Republic of China; 3Research Center for Child Development and Behavior, Harbin Medical University, Harbin, People’s Republic of China; 4Department of Pediatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, People’s Republic of China; 5Department of Children Rehabilitation, Hainan Women and Children’s Medical Center, Haikou, People’s Republic of China; 6Children Health Care Center, Xi’an Children’s Hospital, Xi’an, People’s Republic of China; 7The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, People’s Republic of China; 8Department of Child Health Care, The Affiliated Hospital of Qingdao University, Qingdao, People’s Republic of China; 9Maternal and Child Health Hospital of Baoan, Shenzhen, People’s Republic of China; 10Department of Child Health Care, Shanghai Children’s Hospital, Shanghai, People’s Republic of China; 11Department of Child Health Care, Children’s Hospital Affiliated of Zhengzhou University, Zhengzhou, People’s Republic of China; 12National Health Commission Key Laboratory of Birth Defect for Research and Prevention, Hunan Provincial Maternal and Child Health Hospital, Changsha, People’s Republic of China; 13Deyang Maternal and Child Health Hospital, Deyang, People’s Republic of China; 14Department of Children Health Care, Capital Institute of Pediatrics, Beijing, People’s Republic of ChinaCorrespondence: Tingyu Li, Children’s Hospital of Chongqing Medical University, Chongqing, 400014, People’s Republic of China, Tel +86 23 63630913, Fax +86 23 63622754, Email tyli@vip.sina.comPurpose: The present study investigated the age of diagnosis, treatment and demographic factors of Chinese children with autism spectrum disorders (ASD), to provide a scientific basis for the early detection, diagnosis, and intervention of ASD.Patients and Methods: A total of 1500 ASD children aged 2– 7 years old from 13 cities in China were administered questionnaires to examine their diagnosis, treatment, and basic family information. The Childhood Autism Rating Scale (CARS) was used to measure the symptoms and severity of ASD children, and the Children Neuropsychological and Behavior Scale-Revision 2016 (CNBS-R2016) was utilized to measure neurodevelopmental levels of ASD children.Results: We found that for children with ASD, the median (p25, p75) age for the initial detection of social behavioral developmental delay was 24 (18, 30) months, while the age for the initial diagnosis was 29 (24, 36) months and the age for the beginning of intervention was 33 (27, 42) months. Multiple linear regression (MLR) analysis suggested that in children with ASD whose parents were divorced, separated, or widowed, or whose mothers were engaged in physical work, the initial detection of social behavioral developmental delay happened later. For the children with ASD who lived in urban areas, had higher levels of ASD symptom severity or whose parents were not divorced or separated, the age for the initial diagnosis was earlier. For the children with ASD who lived in urban areas or whose mothers had received higher level of education, the earlier age for the beginning of intervention was observed, while for those with ASD whose mothers were engaged in physical work, the age for the beginning of training was later.Conclusion: It is recommended to actively carry out health education of ASD and strengthen the support for ASD families to enhance their rehabilitation level.Keywords: autism spectrum disorders, children, age, influencing factors, multicenter

It is envisioned that 6G, unlike its predecessor 5G, will depart from connected machines and connected people to connected intelligence. The main goal of 6G networks is to support massive connectivity for time-sensitive and computation-sensitive services in mission-critical applications. The creation of real-time optimization (RTO) enabled by the fast growing data analytic and machine learning will seize the opportunities for 6G wireless networks to support such immersive services such as virtual reality (VR), augmented reality (AR), mixed reality (MR), and tactile Internet. Recently, with the rapid development of quantum computers, quantum-inspired optimization and machine learning algorithms have been exploited as efficient solutions for future wireless networks. In this article, we provide a comprehensive view on the new concept of quantum-inspired RTO and its application to the optimal resource allocation for 6G wireless networks. Our main contributions are to introduce some of the initial research results and introduce the potentiality of quantum-inspired RTO on some 6G emerging technologies. Not only do we review the fundamental principles; we also explore the challenges and opportunities of this exciting research direction.

Ultra-dense networks (UDNs) have been employed to solve the pressing problems in relation to the increasing demand for higher coverage and capacity of the fifth generation (5G) wireless networks. The deployment of UDNs in a very large scale has been envisioned to break the fundamental deadlocks of beyond 5G or the sixth generation (6G) networks and deliver many more orders of magnitude gains that today’s technologies achieve. However, the mathematical tool to optimize the system performance under the stringent radio resource constraints is widely recognized to be a formidable challenge. System-level performance optimization of current UDNs are usually conducted by relying on numerical simulations, which are often time-consuming and have become extremely difficult in the context of 6G with extremely high density. As such, there is an urgent need for developing a realistic mathematical model for optimizing the 6G UDNs. In this paper, we introduce challenges as well as issues that have to be thoroughly considered while deploying UDNs in realistic environment. We revisit efficient mathematical techniques including game theory and real-time optimization in the context of optimizing UDNs performance. In addition, emerging technologies which are suitable to apply in UDNs are also discussed. Some of them have already been used in UDNs with high efficiency while the others which are still under investigation are expected to boost the performance of UDNs to achieve the requirements of 6G. Importantly, for the first time, we introduce the joint optimal approach between realtime optimization and game theory (ROG) which is an effective tool to solve the optimization problems of large-scale UDNs with low complexity. Then, we describe two approaches for using ROG in UDNs. Finally, some case study of ROG are given to illustrate how to apply ROG for solving the problems of different applications in UDNs.

A novel carbon quantum dots decorated C-doped α-Bi2O3 photocatalyst (CBO/CQDs) was synthesized by solvothermal method. The synergistic effect of adsorption and photocatalysis highly improved contaminants removal efficiencies. The ceftriaxone sodium degradation rate constant (k) of CBO/CQDs was 11.4 and 3.2 times that of pure α-Bi2O3 and C-doped α-Bi2O3, respectively. The interstitial carbon doping generated localized states above the valence band, which enhanced the utilization of visible light and facilitated the separation of photogenerated electrons and holes; the loading of CQDs improved the charge carrier separation and extended the visible light response; the reduced particle size of CBO/CQDs accelerated the migration of photogenerated carriers. The •O2− and h+ were identified as the dominant reactive species in ceftriaxone sodium degradation, and the key role of •O2− was further investigated by NBT transformation experiments. The Fukui index was applied to ascertain the molecular bonds of ceftriaxone sodium susceptible to radical attack, and intermediates analysis was conducted to explore the possible degradation pathways. The toxicity evaluation revealed that some degradation intermediates possessed high toxicity, thus the contaminants require sufficient mineralization to ensure safe discharge. The present study makes new insights into synchronous carbon dopping and CQDs decoration on modification of α-Bi2O3, which provides references for future studies.

Although the impact of tourism and COVID-19 has been paid much attention, limited research considers the perspectives from local people working in tourism, especially those affected by the pandemic the most. This article examines the perspectives of tourism labors in Nha Trang, Vietnam on the dual impacts of tourism and the COVID-19 pandemic on their lives and the local economy. Semi-structured interviews were conducted with 26 participants working in different fields in a tourism sector. Results revealed that that before the COVID-19 hit Nha Trang, tourism had both positive and negative impacts on local people and economy. These impacts are differently among local people and economy. While tourism had improved the quality of local people lives who ran business relating to tourism activities such as hotels, restaurants, the tourism had damaged the local economy such as overloaded infrastructure and tourist facilities, lack of quality control of tourism, unfair competition between tourism companies, traffic congestion and floods in the city, and “zero-dollar tour”. When the COVID-19 hit, the local tourism firms had broken down, particularly firms target their services at foreign visitors. To deal with these challenges of tourism under the impact of COVID-19, this study also found out sustainable approaches such as diversifying sources of tourists, multiplying local people’s sources of income, and improving the local government’s tourism management. Theoretical and practical implications are discussed.

Quan Meng,1 Can-Xuan Li,2 Dan Long3 1Department of Clinical Laboratory, Shenzhen Traditional Chinese Medical Hospital, Shenzhen, Guangdong, People’s Republic of China; 2Department of Urology, Shenshan Central Hospital, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Shanwei, Guangdong, People’s Republic of China; 3Respiratory Medicine, Shenshan Central Hospital, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Shanwei, Guangdong, People’s Republic of ChinaCorrespondence: Can-Xuan LiDepartment of Urology, Shenshan Central Hospital, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Shanwei, 516600, Guangdong, People’s Republic of ChinaEmail china20170713@163.comAim: The aim of this study was to investigate the expression of CARS (cysteinyl-tRNA synthetase) in clear cell renal cell carcinoma (ccRCC) and its biological action mechanisms.Methods: Expression profiles and clinical information were obtained from The Cancer Genome Atlas (TCGA) to estimate the CARS expression patterns in ccRCC, its relationship with clinicopathological variables, and prognosis of ccRCC and potential biological mechanisms in ccRCC.Results: CARS was significantly elevated in ccRCC. Overexpression of CARS indicated disease progression. Univariate and multivariate Cox regression analyses identified CARS as an independent prognostic factor for overall survival (OS) in renal clear cell carcinoma. Mechanically, CARS influenced the progression of ccRCC through several tumor-related pathways. Additionally, we found that CARS was significantly associated with tumor mutational burden, tumor-infiltrating immune cells, immunosuppressive molecules, methyltransferases, and mismatch repair proteins.Conclusion: CARS could serve as a promising prognostic biomarker and therapeutic target for ccRCC.Keywords: CARS, clear cell renal cell carcinoma, prognosis

Quan Meng,1,&ast; Can-Xuan Li,2,&ast; Dan Long,3 Xiaobin Lin4 1Department of Clinical Laboratory, Shenzhen Traditional Chinese Medical Hospital, Shenzhen, Guangdong, People’s Republic of China; 2Department of urology, Shenshan Central Hospital, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Shanwei, Guangdong, People’s Republic of China; 3Respiratory Medicine, Shenshan Central Hospital, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Shanwei, Guangdong, People’s Republic of China; 4Department of Breast Surgery and General Surgery, Guangzhou Women and Children’s Medical Center, Guangzhou Medical University, Guangzhou, Guangdong, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Xiaobin LinDepartment of Breast Surgery and General Surgery, Guangzhou Women and Children’s Medical Center, Guangzhou Medical University, Guangzhou, Guangdong, People’s Republic of ChinaEmail lxb826463580@163.comAim: The study was designed to mine the expression and roles of IQGAP3 in clear cell renal cell carcinoma (ccRCC).Methods: Expression profiles and clinical information were obtained from the Cancer Genome Atlas (TCGA) to estimate IQGAP3 expression in ccRCC, its relationship with patients’ clinicopathological variables and prognosis, and the potential biological mechanisms.Results: IQGAP3 was highly expressed in ccRCC and indicated advanced clinical outcome and poor prognosis. IQGAP3 affected the progression of ccRCC through several cancer-related pathways. IQGAP3 might play a vital role in the ccRCC tumor microenvironment.Conclusion: IQGAP3 could serve as a promising prognostic biomarker and therapeutic target.Keywords: IQGAP3, clear cell renal cell carcinoma, prognosis

Canxuan Li,1,* Yuzhi Yao,2,3,* Dan Long,4 Xiaobin Lin2,5 1Department of Urology, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, People’s Republic of China; 2Department of Breast Surgery and General Surgery, Guangzhou Women and Children’s Medical Center, Guangzhou, Guangdong, People’s Republic of China; 3Guangzhou Women and Children’s Medical Center, Department of Paediatric Surgery Clinic, Guangzhou, Guangdong, People’s Republic of China; 4Respiratory medicine, Shenshan Central Hospital, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Shanwei, Guangdong, People’s Republic of China; 5Department of Breast Surgery, The First Affiliated Hospital of Jinan University, Guangdong, People’s Republic of China*These authors contributed equally to this workCorrespondence: Xiaobin LinDepartment of Breast Surgery and General Surgery, Guangzhou Women and Children’s Medical Center, Guangzhou, Guangdong, People’s Republic of ChinaEmail lxb826463580@163.comBackground: SARS-CoV-2 proteins binding human mRNAs (SPBRs) have been proven to regulate a variety of tumor-related functions in different types of cancer. However, their biological roles and potential mechanisms in clear cell renal cell carcinoma (ccRCC) are still elusive. Herein, we investigate the expression and prognostic value of SPBRs in ccRCC through bioinformatics methods.Methods: Data downloaded from the Cancer Genome Atlas (TCGA) database was used to screen differentially expressed SPBRs (DE-SPBRs) between ccRCC samples and noncancerous samples. Metascape was utilized to perform function and pathway enrichment analyses of these DE-SPBRs. Kaplan–Meier method of overall survival (OS) was used to assess the prognostic value of DE-SPBRs in ccRCC patients. Univariate and multivariate Cox regression analyses were applied to identify candidate SPBRs, which were independently associated with overall survival of ccRCC patients. Subsequently, several internationally renowned databases were employed to conduct a comprehensive analysis of candidate SPBRs to further investigate their roles and mechanisms in ccRCC.Results: A total of 33 DE-SPBRs, including 18 upregulated SPBRs and 17 downregulated SPBRs, were screened between ccRCC samples and noncancerous samples. Among them, two candidate SPBRs, KDELC1 and TRMT1, were identified. Additionally, we observed that upregulated KDELC1/TRMT1 expression in ccRCC at both gene and protein levels was significantly associated with clinicopathological features. Furthermore, we found that KDELC1/TRMT1 genetic mutation has an unfavorable influence on prognosis of patients with ccRCC. Functional enrichment analysis revealed that KDELC1/TRMT1 was closely enriched in several vital biological processes and pathways. Finally, we noticed that KDELC1/TRMT1 was remarkably associated with immune infiltrates.Conclusion: In summary, we screened DE-SPBRs of ccRCC, which were enriched mainly in various biological and signaling pathways with tumor progression. Furthermore, we identified two candidate DE-SPBRs (KDELC1 and TRMT1), which could serve as promising biomarkers and therapeutic targets of patients with ccRCC.Keywords: clear cell renal cell carcinoma, bioinformatics, SARS-CoV-2 proteins binding human mRNAs

In this study, we consider the combination of clustering and resource allocation based on game theory in ultra-dense networks that consist of multiple macrocells using massive multiple-input multiple-output and a vast number of randomly distributed drones serving as small-cell base stations. In particular, to mitigate the intercell interference, we propose a coalition game for clustering small cells, with the utility function being the ratio of signal to interference. Then, the optimization problem of resource allocation is divided into two subproblems: subchannel allocation and power allocation. We use the Hungarian method, which is efficient for solving binary optimization problems, to assign the subchannels to users in each cluster of small cells. Additionally, a centralized algorithm with low computational complexity and a distributed algorithm based on the Stackelberg game are provided to maximize the network energy efficiency (EE). The numerical results demonstrate that the game-based method outperforms the centralized method in terms of execution time in small cells and is better than traditional clustering in terms of EE.

Yang Sun, Di Long Department of Orthopaedics, Affiliated Central Hospital of Shenyang Medical College, Shenyang, People’s Republic of ChinaCorrespondence: Yang Sun Email sunyang797979@163.comBackground: The present work is an effort to develop a novel locally injection LVTT-loaded PLGA microspheres (LVTT-PLGA-MS) on the treatment of rabbits with femoral head necrosis (FHN).Methods: LVTT-loaded PLGA microspheres (LVTT-PLGA MS) were prepared by an emulsion-solvent evaporation method. The physicochemical properties of LVTT-PLGA-MS were investigated to ensure that they have good qualities and are suitable for local delivery. In vitro drug release behavior of MS was also studied compared with free LVTT. In vivo, we also studied the pharmacokinetics and pharmacodynamics of MS in rabbits with the optimized formulation.Results: In this study, we used the emulsion-solvent evaporation method to prepare LVTT-PLGA MS. Scanning electron microscopy demonstrated that the LVTT-PLGA MS were regular, round in shape and relatively unified size distributions were selected. The mean PS was 12.3± 2.1 μm. The drug-loading rate (27.6% ± 2.9%) was calculated for three batches of MS. The thermogram of LVTT-PLGA MS showed an endothermic peak at 98.3°C, revealing that LVTT existed in MS in an uncrystallized rather than a crystallized form. In the release study, LVTT-PLGA MS is observed linear prolonging drug release rates for more than 21 days without initial burst release. The pharmacodynamic results confirmed that the LVTT-PLGA MS had a good and lasting improvement effect against femoral head necrosis.Conclusion: Our results demonstrated that LVTT-PLGA MS has the potential for being a local delivery system.Keywords: lovastatin, PLGA, microspheres, in vitro/in vivo

We propose an unmanned aerial vehicle (UAV) communications scheme with spectrum-sharing mechanism to provide mission-critical services such as disaster recovery and public safety. Specifically, the UAVs can serve as flying base stations to provide extended network coverage for the affected area under spectrum-sharing cognitive radio networks (CRNs). To cope with the effects of network destruction in a disaster, we propose a real-time optimisation framework for resource allocation (e.g., power and number of UAVs) for CRNs assisted by UAV relays. The proposed optimisation scheme aims at optimising the network throughput of primary and secondary networks under the stringent constraint of maximum tolerable interference impinged on the primary users. We also propose a deep neural network (DNN) model to significantly reduce the execution time under real-time solution of mixed-integer UAV deployment problems. For both primary and secondary networks, our real-time optimisation algorithms impose low computational complexity, hence, have a low execution time in solving throughput optimisation problems, which demonstrates the benefit of our approached proposed for spectrum-sharing UAV-assisted mission-critical services.

Unmanned aerial vehicle (UAV)-assisted device-to-device (D2D) communications can be deployed flexibly thanks to UAVs' agility. By exploiting the direct D2D interaction supported by UAVs, both the user experience and network performance can be substantially enhanced at public events. However, the continuous moving of D2D users, limited energy and flying time of UAVs are impediments to their applications in real-time. To tackle this issue, we propose a novel model based on deep reinforcement learning in order to find the optimal solution for the energy-harvesting time scheduling in UAV-assisted D2D communications. To make the system model more realistic, we assume that the UAV flies around a central point, the D2D users move continuously with random walk model and the channel state information encountered during each time slot is randomly time-variant. Our numerical results demonstrate that the proposed schemes outperform the existing solutions. The associated energy efficiency game can be solved in less than one millisecond by an off-the-shelf processor using trained neural networks. Hence our deep reinforcement learning techniques are capable of solving real-time resource allocation problems in UAV-assisted wireless networks.

Nha Trang Coast is located in the South Central Vietnam and the coastal erosion has occurred rapidly in recent years. Hence it is crucial to accurately monitor the shoreline changes for better coastal management and reduction of risks for communities. In this paper, we explored a statistical forecasting model, Seasonal Auto-regressive Integrated Moving Average (SARIMA), and two Machine Learning (ML) models, Neural Network Auto-Regression (NNAR) and Long Short-Term Memory (LSTM), to predict the shoreline variations from surveillance camera images. Compared to the Empirical Orthogonal Function (EOF), the most common method used for predicting shoreline changes from cameras, we demonstrate that the SARIMA, NNAR and LSTM models outperform the EOF model significantly in terms of prediction accuracy. The forecasting performance of the SARIMA model, NNAR model and LSTM model is comparable in both long and short-term predictions. The results suggest that these models are highly effective in detecting shoreline changes from video cameras under extreme weather conditions.

This study examined membrane fouling and associated microbial taxa in a membrane bioreactor operating at a sub-critical flux condition using next-generation amplicon sequencing. The membrane was operated at a sub-critical flux, thus, fouling was not observed until endogenous decay. The observed fouling could be attributed to endogenous decay which was driven by nutrient deficiency at high sludge age and low food-to-microorganisms ratio (decreasing from 0.15 to 0.09 gBOD/gMLVSS.d). Endogenous decay resulted in a sharp decrease of the number of species and evenness between different species (49.7 and 58.9% compared to the inoculum, respectively). The release of dissolved organic matters and cell debris from endogenous decay as well as the excessive growth of filamentous bacteria, e.g. Thiotrichales were the main contributors to membrane fouling. The relative abundance of Thiotrichales significantly correlated with TMP (Pearson R = 0.996, p-value

Dan Li,1,2,* Wenjian Liao,1,2,* Hai-hua Huang,3 Fang-ling Du,4 Dan-dan Wei,4 Yan-fang Mei,4 Dan Long,4 La-gen Wan,4 Yang Liu,4 Wei Zhang1 1Department of Respiratory and Critical Care Medicine, First Affiliated Hospital of Nanchang University, Nanchang University, Nanchang 330006, People’s Republic of China; 2The First Clinical Medical College of Nanchang University, Nanchang University, Nanchang 330006, People’s Republic of China; 3Department of Endocrinology and Metabolism, First Affiliated Hospital of Nanchang University, Nanchang University, Nanchang 330006, People’s Republic of China; 4Department of Clinical Microbiology, First Affiliated Hospital of Nanchang University, Nanchang University, Nanchang 330006, People’s Republic of China*These authors contributed equally to this workCorrespondence: Yang LiuDepartment of Clinical Microbiology, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi 330006, People’s Republic of ChinaTel +86-13576091584Fax +86-0791-88692748Email ly13767160474@sina.comWei ZhangDepartment of Respiratory Medicine, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi 330006, People’s Republic of ChinaTel +86-13707089183Fax +86-0791-88692748Email zhangweiliuxin@163.comIntroduction: Carbapenem-resistant hypervirulent Klebsiella pneumoniae (CR-hvKP) is increasingly reported worldwide, but ceftazidime/avibactam (CAZ/AVI)-resistant hvKP isolates have rarely been observed. We attempted to characterize them in clinical CRKP isolates collected from a university hospital in China from March 2016 to March 2018.Methods: All isolates were analyzed by antimicrobial susceptibility testing, molecular detection of antibiotic resistance determinants, multilocus sequence typing (MLST), SDS-PAGE, and pulsed-field gel electrophoresis (PFGE). The pLVPK-related genetic loci (rmpA2, terW, iutA, and silS) were screened in all CAZ/AVI-resistant CRKP isolates for the presence of virulence plasmids by PCR. Capsule typing, serum killing assay, Galleria mellonella lethality experiments, and mouse lethality assay were conducted to identify CAZ/AVI-resistant hvKP among isolates that carried all four virulence genes.Results: A total of 232 CRKP isolates were collected. Overall, CAZ/AVI-resistance was found in 8.2% (19/232) CRKP isolates isolated from patients with no history of previous CAZ/AVI-based treatment. Among these, 63.2% (12/19) were metallo-β-lactamase-producing K. pneumoniae (MBL-KP), 52.6% (10/19) were Klebsiella pneumoniae carbapenemase (KPC)-producing K. pneumoniae (KPC-KP), and 26.3% (5/19) produced both MBL and KPC. The presence of carbapenemase promoted a very high increase in CAZ/AVI minimum inhibitory concentration only when ompk35 and ompk36 were absent. Alarmingly, nine isolates had all four virulence genes for the presence of virulence plasmids. All nine isolates were considered to be CAZ/AVI-resistant hvKP according to the G. mellonella infection model and mouse lethality assay, with ST23 being the most common type (55.6%, 5/9).Conclusion: The newly emerged hypervirulent CAZ/AVI-resistant KP strain might cause a serious threat to public health, suggesting an urgent need for enhanced clinical awareness and epidemiologic surveillance.Keywords: ceftazidime/avibactam-resistant, hypervirulent, Klebsiella pneumoniae, ompk35

A filtration method was used to prepare buckypaper (BP) membranes for water desalination. The samples are made from a biopolymer (chitosan) containing functionalized multiwall carbon nanotubes (MWCNTs) with –NH2 and −COOH moieties. Comprehensive characteristics of all BP membranes were systematically investigated. This included optimisation of sonication time, electrical and mechanical properties (ranging from 7 ± 1 to 69 ± 1 S/cm and tensile strengths between 17 ± 2 and 60 ± 2 MPa, respectively), contact angle (36 ± 3° to 105°±2°), morphology, surface area (ranging from 12 ± 2 m2/g to 112 ± 4 m2/g), surface charge, water permeability (from 0.5 ± 0.1 to 6.5 ± 0.2 L/m2 h bar), and salt rejection. The results indicate that the properties of the BP membrane have been significantly affected as the top layer by the type of multi-walled carbon nanotubes. With MWCNTs−COOH / chitosan BP membranes, the highest permeability towards water was achieved while the MWCNTs-NH2/chitosan membranes provided the best performance in salt rejection by properly balancing amine groups on the top layer, which could be separate monovalent and multivalent cations from salt solutions. Moreover, for monovalent cations (e.g. Na+) BP membranes (MWCNTs/chitosan and MWCNTs-NH2/chitosan) with sufficiently balanced amine groups on the top layer are shown to be much bigger rejection than for higher-valent cations (e.g. Mg2+).

Among many multi-carrier systems, circular filter-bank multi-carrier offset quadrature amplitude modulation (CFBMC-OQAM) is one of promising candidates for future wireless communications. This paper studies the impact of phase noise and its compensation for CFBMC-OQAM under imperfect channel estimation, which has not been done before. In the presence of phase noise, a two-stage phase noise compensation algorithm is proposed. In the first stage, the channel frequency response and phase noise are estimated based on the transmission of a preamble. Such a preamble is designed to minimize the channel mean squared error. In the second stage, the estimated channel obtained from the first stage together with pilot symbols are used to compensate for the phase noise and detect the transmitted signal. Simulation results obtained under practical scenarios show that the proposed algorithm effectively estimates the channel frequency response and compensates for the phase noise. The proposed algorithm is also shown to outperform an existing algorithm that performs iterative phase noise compensation when phase noise impact is high.

The recent outbreak of the coronavirus disease 2019 (COVID-19) has rapidly become a pandemic, which calls for prompt action in identifying suspected cases at an early stage through risk prediction. To suppress its further spread, we exploit the social relationships between mobile devices in the Social Internet of Things (SIoT) to help control its propagation by allocating the limited protective resources to the influential so-called high-degree individuals to stem the tide of precipitated spreading. By exploiting the so-called differential contact intensity and the infectious rate in susceptible-exposed-infected-removed (SEIR) epidemic model, the resultant optimization problem can be transformed into the minimum weight vertex cover (MWVC) problem of graph theory. To solve this problem in a high-dynamic random network topology, we propose an adaptive scheme by relying on the graph embedding technique during the state representation and reinforcement learning in the training phase. By relying on a pair of real-life datasets, the results demonstrate that our scheme can beneficially reduce the epidemiological reproduction rate of the infection. This technique has the potential of assisting in the early identification of COVID-19 cases.

Compensation for the impacts of phase noise (PN) and in-phase and quadrature (IQ) imbalance on cyclic-prefix (CP) based multi-carrier modulation systems in the presence of imperfect channel estimation is considered in this paper. A unified two-stage algorithm is proposed. In the first stage, IQ imbalance parameters and channel impulse response are estimated based on the transmission of a preamble which is designed in such a way that the estimation of IQ imbalance does not require any knowledge about the channel and PN. Given the estimates from the first stage, the impacts of IQ imbalance and PN are subsequently compensated in the second stage based on the transmission of pilot symbols. The proposed algorithm is further extended to a MIMO system that employs a diversity technique. Simulation results are presented for a wide range of PN and IQ imbalance scenarios to corroborate the effectiveness of the proposed algorithm.

Single use personal protective equipment (PPE) has played a major role in preventing COVID-19 infection. Since the beginning of the COVID-19 pandemic, over 4 million tonnes of polypropylene PPE waste has been disposed into the environment in uncontrolled manner causing significant and long-term ecological damage. This work also highlights several effective measures to alleviate the problem of polypropylene PPE waste. Short-term measures include knowledge sharing to minimise the use of single use PPE and to adapt innovative polypropylene recycling technologies. To prepare for a future pandemic, it is also essential to phase out polypropylene PPE using natural based polymers.

This study explored the use of forward osmosis for extracting water from rumen fluid to replicate the function of the ruminant digestive system. A recipe for artificial saliva was used to prepare draw solution for extracting water from rumen fluid. The osmotic pressure of artificial saliva and rumen fluid were compared to determine the reference artificial saliva solution. High water flux and a low reverse solute flux (RSF) were observed using artificial saliva concentration as a draw solution and clean water as the feed. Water flux decreased and the RSF increased when using rumen fluid as the feed due to membrane fouling.

The lack of communication between local authorities, first aid responders, and the population that are present in a natural disaster area, represents critical aspects which can compromise relief operations in saving human lives. During natural disasters (earthquakes/tsunamis), the typical telecommunications network infrastructure in the affected area could be damaged or unfunctional. This can seriously compromise the efficiency of first aid operations. In this paper, we propose a device-to-device (D2D)-based framework which, starting from some basic information such as positions and battery level of victim's devices, could provide communication from a disaster area towards a functional area. This framework, utilized by a base station located in a functional area, organizes users of disaster area into clusters of users and for each cluster select a gateway. This framework permits also, to evaluate the optimal transmission power for each gateway in order to maximize the energy efficiency in the area and to create a multi-hop path from the disaster area to relay node minimizing the end-to-end delay. The simulations results demonstrate that our proposed approach outperforms either random policy assignment and static policies assignment in both power allocation and routing path creations.

Unmanned aerial vehicles (UAVs) have acquired remarkable popularity, thanks to their variety of applications in numerous domains spanning from surveillance, health to agriculture and smart cities. UAVs are also enabler in wireless communication that has potential features such as ubiquitous and reliable connectivity, fast and easy deployment, adaptive altitude, higher chance of line of sight (LOS) propagation path, higher mobility and flexibility. There are numerous surveys that summarized these advantages for different situations and scenarios. However, none of these surveys discussed the role of UAVs in public safety communications from the energy efficiency perspective. In this paper, we review the existing literature for UAV communication with taking into account the energy consumption criteria, and propose a multi-layered network architecture incorporating UAVs for public safety communication. Future research directions are also discussed.

Device-to-device (D2D) communication is an emerging technology in the evolution of the 5G network enabled vehicle-to-vehicle (V2V) communications. It is a core technique for the next generation of many platforms and applications, e.g. real-time high-quality video streaming, virtual reality game, and smart city operation. However, the rapid proliferation of user devices and sensors leads to the need for more efficient resource allocation algorithms to enhance network performance while still capable of guaranteeing the quality-of-service. Currently, deep reinforcement learning is rising as a powerful tool to enable each node in the network to have a real-time self-organising ability. In this paper, we present two novel approaches based on deep deterministic policy gradient algorithm, namely “distributed deep deterministic policy gradient” and “sharing deep deterministic policy gradient”, for the multi-agent power allocation problem in D2D-based V2V communications. Numerical results show that our proposed models outperform other deep reinforcement learning approaches in terms of the network's energy efficiency and flexibility.

This article discusses the potential ramifications of the COVID-19 pandemic on waste and wastewater services, focusing on critical points where alternative operating procedures or additional mitigation measures may be advisable. Key concerns are (i) the long half-life of the virus on materials such as waste containers, bags, and in wastewater, and (ii) possible transmission via contaminated waste surfaces and aerosols from wastewater systems. There are opportunities to further the science of wastewater-based epidemiology by monitoring viral RNA in wastewater to assess disease prevalence and spread in defined populations, which may prove beneficial for informing COVID-19 related public health policy.

Special events can impose burdens to local roads. As these events are temporary and even seasonal in nature, concerned agencies need to identify cost-effective traffic management strategies to control this increased traffic. The current research empirically investigated the traffic flow, traffic volumes, and traffic management strategies for sporting events in Fort Myers, Florida. Extensive data were collected for over five consecutive years on an arterial road. These data contained traffic volumes from available loop detectors and travel time from Bluetooth sensors. Results showed that, like the experience curve effect, manual traffic control seemed to improve the traffic after the first year but leveled off thereafter. Signal retiming was effective for traffic entering games but not after games. The average travel time on a certain road segment for through traffic before the event starts was reduced by >40% after the signal retiming. Variable message signs (VMS), while appeared to help traffic management, might not considerably improve travel time before and after the events on the road investigated. Although an alternative route was introduced for through traffic, most of drivers still used the arterial road even during peak congestion. With an average penetration rate of >4%, this long-term study confirmed that the use of Bluetooth-based systems in collecting traffic probe data are still feasible in the near future. This current study contributes to the traffic management body of knowledge by empirically investigating the traffic management plans used for sporting events with objective and quantitative data in a five-year period.

BACKGROUND: Plasma proenkephalin A (PENK-A) is a precursor of active enkephalins. Higher blood concentrations have been associated with estimated glomerular filtration rate (eGFR) decline in European populations. Due to the significant disparity in incident chronic kidney disease (CKD) between White and Black people, we evaluated the association of PENK-A with incident CKD and other kidney outcomes among a biracial cohort in the U.S. METHODS: In a nested cohort of 4,400 participants among the REasons for Geographic And Racial Differences in Stroke, we determined the association between baseline PENK-A concentration and incident CKD using the creatinine-cystatin C CKD-EPI 2021 equation without race coefficient, significant eGFR decline, and incident albuminuria between baseline and a follow-up visit 9.4 years later. We tested for race and sex interactions. We used inverse probability sampling weights to account for the sampling design. RESULTS: At baseline, mean (SD) age was 64 (8) years, 49% were women, and 52% were Black participants. 8.5% developed CKD, 21% experienced ≥ 30% decline in eGFR and 18% developed albuminuria. There was no association between PENK-A and incident CKD and no difference by race or sex. However, higher PENK-A was associated with increased odds of progressive eGFR decline (OR: 1.12; 95% CI 1.00, 1.25). Higher PENK-A concentration was strongly associated with incident albuminuria among patients without diabetes mellitus (OR: 1.29; 95% CI 1.09, 1.53). CONCLUSION: While PENK-A was not associated with incident CKD, its associations with progression of CKD and incident albuminuria, among patients without diabetes, suggest that it might be a useful tool in the evaluation of kidney disease among White and Black patients.

Epidemiology of hepatocellular carcinoma (HCC) showed a correlation between incidence and geographical-relevant risk factors. This study aims to compare the distributions of cancer stem cells (CSC) in two distant populations in Asia and Europe. We analyzed 52 and 43 selected HCC patients undergoing hepatectomy in Ho Chi Minh City (Vietnam) and Trieste (Italy). Each patient sample consisted of HCC, peri-HCC, and non-tumoral (distal) tissue. Demographic data were recorded together with clinical findings. The protocol for the collection of tissue samples and RNA was standardized in both laboratories and gene expression analysis was performed in a single laboratory with identical PCR conditions. Baseline data showed comparable laboratory findings between the two cohorts. mRNA distribution showed a comparable pattern of all CSC markers analyzed with the expression of CD90 progressively increasing from distal and peri-HCC to be highest in HCC (p < 0.001), confirmed by immunofluorescence data. CD90 mRNA distribution was related to HBV-related HCC and a tumor diameter less than 5 cm. Patients with high tumoral CD90 mRNA had a shorter time (p < 0.05) to tumor recurrence compared to patients with lower CD90. This comparative study showed that CD90 mRNA expressions are comparable between Eastern and Western HCC cases.

This paper considers a heterogeneous network, which consists of one macro base station and numerous small cell base stations (SBSs) cooperatively serving multiple user terminals. The first objective is to design cooperative transmit beamformers at the base stations to maximize the network energy efficiency (EE) in terms of bits per joule subject to the users' quality of service (QoS) constraints, which poses a computationally difficult optimization problem. The commonly used Dinkelbach-type algorithms for optimizing a ratio of concave and convex functions are not applicable. This paper develops a path-following algorithm to address the computational solution to this problem, which invokes only a simple convex quadratic program of moderate dimension at each iteration and quickly converges at least to a locally optimal solution. Furthermore, the problem of joint beamformer design and SBS service assignment in the three-objective (EE, QoS, and service loading) optimization is also addressed. Numerical results demonstrate the performance advantage of the proposed solutions.

Underwater Wireless Sensor Networks (UWSNs) are an enabling technology for many applications in commercial, military, and scientific domains. In some emergency response applications of UWSN, data dissemination is more important, therefore these applications are handled differently as compared to energy-focused approaches, which is only possible when propagation delay is minimized and packet delivery at surface sinks is assured. Packet delivery underwater is a serious concern because of harsh underwater environments and the dense deployment of nodes, which causes collisions and packet loss. Resultantly, re-transmission causes energy loss and increases end-to-end delay ( D E 2 E ). In this work, we devise a framework for the joint optimization of sink mobility, hold and forward mechanisms, adoptive depth threshold ( d t h ) and data aggregation with pattern matching for reducing nodal propagation delay, maximizing throughput, improving network lifetime, and minimizing energy consumption. To evaluate our technique, we simulate the three-dimensional (3-D) underwater network environment with mobile sink and dense deployments of sensor nodes with varying communication radii. We carry out scalability analysis of the proposed framework in terms of network lifetime, throughput, and packet drop. We also compare our framework to existing techniques, i.e., Mobicast and iAMCTD protocols. We note that adapting varying d t h based on node density in a range of network deployment scenarios results in a reduced number of re-transmissions, good energy conservation, and enhanced throughput. Furthermore, results from extensive simulations show that our proposed framework achieves better performance over existing approaches for real-time delay-intolerant applications.

Vu Gia-Thu Bon (VGTB) river basin is an area where flash flood and heavy flood events occur frequently, negatively impacting the local community and socio-economic development of Quang Nam Province. In recent years, structural and non−structural solutions have been implemented to mitigate damages due to floods. However, under the impact of climate change, natural disasters continue to happen unpredictably day by day. It is, therefore, necessary to develop a spatial decision support system for real-time flood warnings in the VGTB river basin, which will support in ensuring the area’s socio-economic development. The main purpose of this study is to develop an online flood warning system in real-time based on Internet-of-Things (IoT) technologies, GIS, telecommunications, and modeling (Soil and Water Assessment Tool (SWAT) and Hydrologic Engineering Center’s River Analysis System (HEC−RAS)) in order to support the local community in the vulnerable downstream areas in the event of heavy rainfall upstream. The structure of the designed system consists of these following components: (1) real-time hydro-meteorological monitoring network, (2) IoT communication infrastructure (Global System for Mobile Communications (GSM), General Packet Radio Service (GPRS), wireless networks), (3) database management system (bio-physical, socio-economic, hydro-meteorological, and inundation), (4) simulating and predicting model (SWAT, HEC−RAS), (5) automated simulating and predicting module, (6) flood warning module via short message service (SMS), (7) WebGIS, application for providing and managing hydro-meteorological and inundation data, and (8) users (citizens and government officers). The entire operating processes of the flood warning system (i.e., hydro-meteorological data collecting, transferring, updating, processing, running SWAT and HEC−RAS, visualizing) are automated. A complete flood warning system for the VGTB river basin has been developed as an outcome of this study, which enables the prediction of flood events 5 h in advance and with high accuracy of 80%.

In post-disaster scenarios, such as after floods, earthquakes, and in war zones, the cellular communication infrastructure may be destroyed or seriously disrupted. In such emergency scenarios, it becomes very important for first aid responders to communicate with other rescue teams in order to provide feedback to both the central office and the disaster survivors. To address this issue, rapidly deployable systems are required to re-establish connectivity and assist users and first responders in the region of incident. In this work, we describe the design, implementation, and evaluation of a rapidly deployable system for first response applications in post-disaster situations, named RDSP. The proposed system helps early rescue responders and victims by sharing their location information to remotely located servers by utilizing a novel routing scheme. This novel routing scheme consists of the Dynamic ID Assignment (DIA) algorithm and the Minimum Maximum Neighbor (MMN) algorithm. The DIA algorithm is used by relay devices to dynamically select their IDs on the basis of all the available IDs of networks. Whereas, the MMN algorithm is used by the client and relay devices to dynamically select their next neighbor relays for the transmission of messages. The RDSP contains three devices; the client device sends the victim’s location information to the server, the relay device relays information between client and server device, the server device receives messages from the client device to alert the rescue team. We deployed and evaluated our system in the outdoor environment of the university campus. The experimental results show that the RDSP system reduces the message delivery delay and improves the message delivery ratio with lower communication overhead.

Resource allocation is one important mission in wireless communication systems. In 5G wireless networks, it is essential that the new systems be more dynamic and wiser to simultaneously satisfy various network demands, by using new wireless technologies and approaches. To this end, resource allocation is faced with many significant challenges such as interference alignment, security attacks, or green communication. On the other hand, as one serious problem in 5G networks, the issue of energy is aected directly by the allocated resources in the system, i.e., bandwidth allocation, power control, association allocation, and deployment strategies. Consequently, together with the enhancement of spectral eÿciency performance, an emerging trend of 5G wireless networks is to approach green communication via energy eÿciency (EE) (bits/Hz/Joule), whose most significant challenge is due to its belonging to the fractional programming in the optimization field, i.e., nonconvex programming. This leaves many diÿcult tasks for improving network EE performance. In this paper, we will tackle the critical EE in 5G wireless networks.