Per- and polyfluoroalkyl substances (PFASs) in bivalve molluscs from Shandong Province, China: Occurrence, distribution, and implications for human consumption.

We examined the occurrence and levels of 19 legacy and emerging per- and polyfluoroalkyl substances (PFASs) in 7 species of marine bivalve molluscs collected from four coastal cities of Shandong Province, China. Perfluorooctanoic acid (PFOA) was the most prevalent component, accounting for 68.1 % of total PFASs. The total PFASs in bivalve molluscs ranged from 0.86 to 6.55 ng/g wet weight, with the highest concentration found in Meretrix meretrix L. The concentration of total PFASs in bivalve molluscs showed the following trend: clams > scallops > oysters > mussels. Estimation on the human intake of PFASs from consumption of bivalve molluscs resulted in hazard ratios (HR) ranging from 0.12 to 6.40. Five of the seven species had HR >1, indicating high exposure risks associated with PFASs. Therefore, the occurrence of PFASs in marine biota is particularly concerning and further investigations on the sources of PFASs in Shandong are warranted.

Diagnosis of DC series arc fault based on multiple wavelet transform and optimal fractional wavelet energy entropy.

In the DC distribution system, the propagation of arc noise can interfere with normal lines, and accurate and timely diagnosis of the location of series arc fault (SAF) is a challenging problem. In this article, a SAF diagnosis method is proposed from a system perspective, which can accurately identify the fault line. First, multiple wavelet transform is used to decompose the currents of different lines, and the fractional wavelet energy entropy is extracted to construct the feature vector. Then, random forest is employed to analyze the importance of features and to select the optimal features. Finally, a kernel extreme learning machine can fuse the features and output the diagnosis results. The offline experimental results indicate that the proposed method has a diagnosis accuracy of 99.82%, which is higher than those of nine comparison methods, and the effectiveness and advancement of the proposed method are verified. The online experimental results show that the proposed method can diagnose SAF within 110 ms, and the diagnosis speed is able to satisfy the requirements of UL1699B. Moreover, under transient conditions, the proposed method can effectively avoid false alarms and maintain stability.

Comparative investigation on heterotrophic denitrification driven by different biodegradable polymers for nitrate removal in mariculture wastewater: Organic carbon release, denitrification performance, and microbial community.

Heterotrophic denitrification is widely studied to purify freshwater wastewater, but its application to seawater wastewater is rarely reported. In this study, two types of agricultural wastes and two types of synthetic polymers were selected as solid carbon sources in denitrification process to explore their effects on the purification capacity of low-C/N marine recirculating aquaculture wastewater (NO3 --N 30 mg/L, salinity 32‰). The surface properties of reed straw (RS), corn cob (CC), polycaprolactone (PCL) and poly3-hydroxybutyrate-hydroxypropionate (PHBV) were evaluated by Brunauer-Emmett-Teller, Scanning electron microscope and Fourier-transform infrared spectroscopy. Short-chain fatty acids, dissolved organic carbon (DOC), and chemical oxygen demand (COD) equivalents were used to analyze the carbon release capacity. Results showed that agricultural waste had higher carbon release capacity than PCL and PHBV. The cumulative DOC and COD of agricultural waste were 0.56-12.65 and 1.15-18.75 mg/g, respectively, while those for synthetic polymers were 0.07-1.473 and 0.045-1.425 mg/g, respectively. The removal efficiency of nitrate nitrogen (NO3 --N) was CC 70.80%, PCL 53.64%, RS 42.51%, and PHBV 41.35%. Microbial community analysis showed that Proteobacteria and Firmicutes were the most abundant phyla in agricultural wastes and biodegradable natural or synthetic polymers. Quantitative real-time PCR indicated the conversion from nitrate to nitrogen was achieved in all four carbon source systems, and all six genes had the highest copy number in CC. The contents of medium nitrate reductase, nitrite reductase and nitrous oxide reductase genes in agricultural wastes were higher than those in synthetic polymers. In summary, CC is an ideal carbon source for denitrification technology to purify low C/N recirculating mariculture wastewater.

Organic carbon release, denitrification performance and microbial community of solid-phase denitrification reactors using the blends of agricultural wastes and artificial polymers for the treatment of mariculture wastewater.

This paper explored the possibility of heterotrophic denitrification driven by composite solid carbon sources in low carbon/nitrogen ratio marine recirculating aquaculture wastewater. In this study, two agricultural wastes, reed straw (RS), corn cob (CC) and two artificial polymers, polycaprolactone (PCL), poly3-hydroxybutyrate-hydroxypropionate (PHBV) were mixed in a 1:1 ratio to compare the carbon release characteristics of the four composite carbon sources (RS+PCL, RS+PHBV, CC+PCL, and CC+PHBV) and their effects on improving the mariculture wastewater for denitrification. Dissolved organic carbon (DOC) after carbon source release (4.96-1.07 mg/g), total organic carbon/chemical oxygen demand (1.9-0.79) and short-chain fatty acids (SCFAs) (4.23-0.21 mg/g) showed that all the four composite solid carbon sources had excellent organic carbon release ability, and the CC+PCL group had the highest release of DOC and SCFAs. Energy-dispersive X-ray spectroscopy, scanning electron microscopy, and Fourier-transform infrared spectroscopy were used to observe the changes in the surface characteristics of the composite carbon source before and after application. And results showed that the stable internal structure enabled CC+PCL group to have continuous carbon release performance and achieved the maximum denitrification efficiency (93.32 %). The NRE results were supported by the abundance of the Proteobacteria microbial community at the phylum level and Marinobacter at the genus level. Quantitative real-time PCR (q-PCR) indicated CC-containing composite carbon source groups have good nitrate reduction ability, while PCL-containing composite carbon source groups have better nitrite reduction level. In conclusion, the carbon source for agricultural wastes and artificial polymers can be used as an economic and effective solid carbon source for denitrification and treatment of marine recirculating aquaculture wastewater.

Fabrication of Ultra-Fine Micro-Vias in Non-Photosensitive Polyimide for High-Density Vertical Interconnects.

With the growing demands for transferring large amounts of data between components in a package, it is required for advanced packaging technologies to form smaller vertical vias in the insulators. Plasma etching is one of the most widely used micro-vias formation processes. This paper has developed a fabrication process for 5-10 µm residue-free micro-vias with 70° tapered angle in polyimide film based on O2/CHF3 inductively coupled plasma (ICP). The etch rate would monotonically increase with the ICP power, RF power, and gas flow rate. As for the gas ratio, there is an optimum range of CHF3 ratio, which could obtain the highest etch rate. The results have clearly shown that the enhancement of ion bombardment and prolongation of etching time would be beneficial to grass-like residue removal. In addition, during the etching of partially cured polyimide, the lateral etch rate would significantly increase in the region near the metal hard mask.

Synthesis of the Magnetically Nanoporous Organic Polymer Fe3O4@SiO2-NH2-COP and Its Application in the Determination of Sulfonamide Residues in Surface Water Surrounding a Cattle Farm.

Efficient extractions of trace antibiotic residues in the environment are a key factor for accurate quantification of the residues. A new nanoporous material, namely, magnetically covalent organic polymer (MCOP, Fe3O4@SiO2-NH2-COP) was synthesized in this work and was used for magnetic solid-phase extraction (MSPE). The combination of MSPE with high-performance liquid chromatography separation together with ultraviolet detection (HPLC-UV) was established as an effective method for the determination of four sulfonamide (SA) residues in surface water surrounding a cattle farm. The synthesized magnetic material was characterized by SEM, TEM, FT-IR, magnetic properties measurement system (MPMS), and nitrogen gas porosimetry. The material possessed many attractive features, such as a unique microporous structure, a larger specific surface area (137.93 m2·g-1) than bare Fe3O4 (24.84 m2·g-1), high saturation magnetization (50.5 emu·g-1), open adsorption sites, and high stability. The influencing parameters, including pH, the used amount of MCOPs, the type of eluent, adsorption solution, and desorption time, were optimized. Under the optimized conditions, the method conferred good linearity ranges (R 2 ≥ 0.9990), low detection limits (S/N = 3, LOD, 0.10-0.25 µg·L-1), and satisfactory recoveries (79.7% to 92.2%). The enrichment factor (EF) for the four SAs was 34.13-38.86. The relative standard deviations of intraday (n = 5) and of interday (n = 3) were less than 4.8% and 8.9%, respectively. The equilibria between extraction and desorption for SAs could be reached within 150 s. The proposed method was sensitive and convenient for detecting SA residues in complex environmental matrices, and the successful application of the new MCOPs as an adsorbent was demonstrated.

Cooperative Spectrum Sensing Based on Multi-Features Combination Network in Cognitive Radio Network.

Cognitive radio, as a key technology to improve the utilization of radio spectrum, acquired much attention. Moreover, spectrum sensing has an irreplaceable position in the field of cognitive radio and was widely studied. The convolutional neural networks (CNNs) and the gate recurrent unit (GRU) are complementary in their modelling capabilities. In this paper, we introduce a CNN-GRU network to obtain the local information for single-node spectrum sensing, in which CNN is used to extract spatial feature and GRU is used to extract the temporal feature. Then, the combination network receives the features extracted by the CNN-GRU network to achieve multifeatures combination and obtains the final cooperation result. The cooperative spectrum sensing scheme based on Multifeatures Combination Network enhances the sensing reliability by fusing the local information from different sensing nodes. To accommodate the detection of multiple types of signals, we generated 8 kinds of modulation types to train the model. Theoretical analysis and simulation results show that the cooperative spectrum sensing algorithm proposed in this paper improved detection performance with no prior knowledge about the information of primary user or channel state. Our proposed method achieved competitive performance under the condition of large dynamic signal-to-noise ratio.

The effective removal of Congo Red using a bio-nanocluster: Fe3O4 nanoclusters modified bacteria.

A bio-nanocluster (Fe3O4@bacteria) was prepared by simply mixture using the bacterial suspension and Fe3O4 nanoclusters to remove Congo red (CR) contamination from water resources. The bio-nanocluster was characterized by SEM, TEM and XPS. Adsorption efficiency, adsorption process and adsorption mechanism were comprehensively investigated. The maximum adsorption capacity (Qm) of CR dye onto the Fe3O4@bacteria peaked at 320.1 mg/g, which was 2.88 times that of Fe3O4 under the same condition. Based on the equilibrium and kinetic studies, the Langmuir isotherm theory and pseudo-first-order model is appropriate to describe the adsorption process. The adsorption of CR is spontaneous and exothermic according to the thermodynamics parameters (ΔGθ, ΔHθ and ΔSθ). The adsorption force dominated the Van der Waals force, biofloculation and chemisorption. The Fe3O4@bacteria could be applied potentially as an absorbent with high efficiency and environmentally friendly remediation of dyeing wastewater.

Abundance and Dynamic Distribution of Antibiotic Resistance Genes in the Environment Surrounding a Veterinary Antibiotic Manufacturing Site.

Background: Antibiotics releasing from the manufacturing sites to the surrounding environment has been identified as a risk factor for the development of antibiotic resistance of bacterial pathogens. However, the knowledge of the abundance and distribution of antibiotic resistance genes (ARGs) influenced by antibiotic pollution is still limited. Methods: In this work, the contamination by resistance genes of the environmental media including an urban river and soil along the river located near the sewage outlet of a veterinary antibiotic manufacturing site in Shijiazhuang, China, was assessed. The abundance and dynamic distribution of ARGs in different sampling points and during different seasons were analyzed using fluorescent quantitative PCR method (qPCR). Results: A total of 11 resistance genes, one integron and one transposon were detected in water and soils around the pharmaceutical factory, and among which, the sulfonamide resistance genes sul1 and ß-lactam resistance genes blaSHV were the most abundant genes. The relative abundance of ARGs in both river water and soil samples collected at the downstream of the sewage outlet was higher than that of samples collected at the upstream, non-polluted areas (p < 0.05). The mobile genetic elements (MGEs) integron in river was significantly correlated (p < 0.05) with the relative abundance of ARGs. Conclusions: The results indicate that the discharge of waste from antibiotic manufacturing site may pose a risk of horizontal transfer of ARGs.

Evaluation of deep learning-based approaches for COVID-19 classification based on chest X-ray images.

The COVID-19, novel coronavirus or SARS-Cov-2, has claimed hundreds of thousands of lives and affected millions of people all around the world with the number of deaths and infections growing exponentially. Deep convolutional neural network (DCNN) has been a huge milestone for image classification task including medical images. Transfer learning of state-of-the-art models have proven to be an efficient method of overcoming deficient data problem. In this paper, a thorough evaluation of eight pre-trained models is presented. Training, validating, and testing of these models were performed on chest X-ray (CXR) images belonging to five distinct classes, containing a total of 760 images. Fine-tuned models, pre-trained in ImageNet dataset, were computationally efficient and accurate. Fine-tuned DenseNet121 achieved a test accuracy of 98.69% and macro f1-score of 0.99 for four classes classification containing healthy, bacterial pneumonia, COVID-19, and viral pneumonia, and fine-tuned models achieved higher test accuracy for three-class classification containing healthy, COVID-19, and SARS images. The experimental results show that only 62% of total parameters were retrained to achieve such accuracy.

Investigation of the microbial community structure and diversity in the environment surrounding a veterinary antibiotic production factory.

The ecological toxicity caused by antibiotic residues and resistance genes in the environment affects the community structures and activities of environmental microorganisms; the ecological toxicity effects of a long-term exposure to low doses antibiotic residues on microorganisms have not however been well-studied. In this work, sequence analysis and species annotation of the full-length 16S rRNA gene were carried out on the extracted whole genome by a 3-generation sequencing method to analyze the diversity of the microbial populations and the population differences among different sampling sites in the environment surrounding a veterinary antibiotic production factory. A total of 1720 OTUs (Operational Taxonomic Unit, OTU) were found, of which 1055 OTUs were in the river samples and 993 OTUs were in the soil samples. 643 and 438 bacterial strains were identified in the river water and soil samples respectively. The bacterial populations are classified into 29 phylum, 612 genus, and 849 species. The dominant phylum of bacteria was Proteobacteria, which was also the absolutely dominant phylum. Shannon diversity index of the bacteria showed that the bacterial abundance in downstream river was significantly higher than that in an upstream non-polluted area (P < 0.001), but the difference of bacterial abundance between soil samples was not significant. There were 61 biomarkers in the river water samples from different sampling points, and 14 biomarkers in soil samples. It was found by the difference statistics of the microbial community that there are multiple biomarkers between this veterinary drug production site and the upstream non-polluted area. Significant differences between multiple functional genes were also found in metabolic pathways of the microorganisms. A similar trend was found for the distribution of antibiotic resistance genes (ARGs). It is concluded that the population composition of microorganisms and diversity are likely related to antibiotic residues and to the distributions of ARGs in the environment surrounding the antibiotic production factory.

Determination of sulfa antibiotic residues in river and particulate matter by field-amplified sample injection-capillary zone electrophoresis.

In the present research, field-amplified sample injection-CZE (FASI-CZE) coupled with a diode array detector was established to determine trace level sulfa antibiotic. Sulfathiazole, sulfadiazine, sulfamethazine, sulfadimethoxine, sulfamethoxazole, and sulfisoxazole were selected as analytes for the experiments. The background electrolyte solution consisted of 70.0 mmol/L borax and 60.0 mmol/L boric acid (including 10% methanol, pH 9.1). The plug was 2.5 mmol/L borax, which was injected into the capillary at a pressure of 0.5 psi for 5 s. Then the sample was injected into the capillary at an injection voltage of -10 kV for 20 s. The electrophoretic separation was carried out under a voltage of +19 kV. The capillary temperature was maintained at 20˚C throughout the analysis, and six sulfonamides were completely separated within 35 min. Compared with pressure injection-CZE, the sensitivity of FASI-CZE was increased by 6.25-10.0 times, and the LODs were reduced from 0.2-0.5 to 0.02-0.05 µg/mL. The method was applied to the determination of sulfonamides in river water and particulate matter samples. The recoveries were 78.59-106.59%. The intraday and interday precisions were 2.89-7.35% and 2.77-7.09%, respectively. This provides a simpler and faster method for the analysis of sulfa antibiotic residues in environmental samples.

Magnetic solid-phase extraction of four ß-lactams using polypyrrole-coated magnetic nanoparticles from water samples by micellar electrokinetic capillary chromatography analysis.

In the present research, the application of a polypyrrole coating on Fe3O4 magnetic nanoparticles (PPy/Fe3O4 MNPs) as a sorbent was confirmed. The synthesized magnetic composites were characterized by TEM, FTIR spectroscopy, and XRD. Four ß-lactams (oxacillin (OXA), cloxacillin (CLOX), dicloxacillin (DIC), and flucloxacillin (FLU)) were selected as analytes for the experiment. The extracted ß-lactams were determined by micellar electrokinetic capillary chromatography-diode array detector (MEKC-DAD). The crucial parameters influencing the extraction efficiency and separation were studied and optimized. Under the optimal conditions, the limits of detection are 1.0 µg L-1 for OXA, CLOX, FLU, and 0.8 µg L-1 for DIC. The calibration curves are linear in the range of 2.5-200.0 µg L-1. The proposed method was applied for the determination of the ß-lactams in water samples with satisfactory results. The intra-day relative standard deviations and the inter-day relative standard deviations range from 1.09% to 4.58% and from 2.95% to 7.80%, respectively. It can be concluded that this method is sensitive, convenient, and feasible for the determination of the ß-lactams in water samples.

Polypyrrole-Modified Nylon 6 Nanofibers as Adsorbent for the Extraction of Two ß-Lactam Antibiotics in Water Followed by Determination with Capillary Electrophoresis.

A solid phase membrane adsorbent-a nylon 6 nanofibers membrane coated by polypyrrole (PPy-PA6-NFsM)-was firstly synthesized and used for extraction of two ß-lactam antibiotics (oxacillin and cloxacillin) in urban river water. Then the analytes were detected by capillary electrophoresis with a diode array detector (CE-DAD). The synthesized nanofibers membrane was characterized by scanning electron microscopy and a Fourier transform infrared spectrometer. The experimental conditions were optimized, including the amount used of PPy-PA6-NFsM, pH of the sample solutions, adsorption volume, and desorption conditions. Under the optimal extraction and separation conditions, the detection limits were found to be 2.0 ng/mL for both oxacillin and cloxacillin. The proposed method was applied to the determination of the two ß-lactams in water samples of an urban river. The recoveries of these two ß-lactams were found to be in the range 84.2-96.4%, demonstrating that PPy-PA6-NFsM has a high extraction capability for these two antibiotics. The relative standard deviations, ranging from 2.26% to 5.29% for intraday measurements and from 2.38% to 7.02% for inter-day determinations, were derived respectively.

Rate Adaptive Based Resource Allocation with Proportional Fairness Constraints in OFDMA Systems.

Orthogonal frequency division multiple access (OFDMA), which is widely used in the wireless sensor networks, allows different users to obtain different subcarriers according to their subchannel gains. Therefore, how to assign subcarriers and power to different users to achieve a high system sum rate is an important research area in OFDMA systems. In this paper, the focus of study is on the rate adaptive (RA) based resource allocation with proportional fairness constraints. Since the resource allocation is a NP-hard and non-convex optimization problem, a new efficient resource allocation algorithm ACO-SPA is proposed, which combines ant colony optimization (ACO) and suboptimal power allocation (SPA). To reduce the computational complexity, the optimization problem of resource allocation in OFDMA systems is separated into two steps. For the first one, the ant colony optimization algorithm is performed to solve the subcarrier allocation. Then, the suboptimal power allocation algorithm is developed with strict proportional fairness, and the algorithm is based on the principle that the sums of power and the reciprocal of channel-to-noise ratio for each user in different subchannels are equal. To support it, plenty of simulation results are presented. In contrast with root-finding and linear methods, the proposed method provides better performance in solving the proportional resource allocation problem in OFDMA systems.

Novel Spectrum Sensing Algorithms for OFDM Cognitive Radio Networks.

Spectrum sensing technology plays an increasingly important role in cognitive radio networks. Consequently, several spectrum sensing algorithms have been proposed in the literature. In this paper, we present a new spectrum sensing algorithm "Differential Characteristics-Based OFDM (DC-OFDM)" for detecting OFDM signal on account of differential characteristics. We put the primary value on channel gain θ around zero to detect the presence of primary user. Furthermore, utilizing the same method of differential operation, we improve two traditional OFDM sensing algorithms (cyclic prefix and pilot tones detecting algorithms), and propose a "Differential Characteristics-Based Cyclic Prefix (DC-CP)" detector and a "Differential Characteristics-Based Pilot Tones (DC-PT)" detector, respectively. DC-CP detector is based on auto-correlation vector to sense the spectrum, while the DC-PT detector takes the frequency-domain cross-correlation of PT as the test statistic to detect the primary user. Moreover, the distributions of the test statistics of the three proposed methods have been derived. Simulation results illustrate that all of the three proposed methods can achieve good performance under low signal to noise ratio (SNR) with the presence of timing delay. Specifically, the DC-OFDM detector gets the best performance among the presented detectors. Moreover, both of the DC-CP and DC-PT detector achieve significant improvements compared with their corresponding original detectors.

Entropy-Based TOA Estimation and SVM-Based Ranging Error Mitigation in UWB Ranging Systems.

The major challenges for Ultra-wide Band (UWB) indoor ranging systems are the dense multipath and non-line-of-sight (NLOS) problems of the indoor environment. To precisely estimate the time of arrival (TOA) of the first path (FP) in such a poor environment, a novel approach of entropy-based TOA estimation and support vector machine (SVM) regression-based ranging error mitigation is proposed in this paper. The proposed method can estimate the TOA precisely by measuring the randomness of the received signals and mitigate the ranging error without the recognition of the channel conditions. The entropy is used to measure the randomness of the received signals and the FP can be determined by the decision of the sample which is followed by a great entropy decrease. The SVM regression is employed to perform the ranging-error mitigation by the modeling of the regressor between the characteristics of received signals and the ranging error. The presented numerical simulation results show that the proposed approach achieves significant performance improvements in the CM1 to CM4 channels of the IEEE 802.15.4a standard, as compared to conventional approaches.

A hybrid multiuser detector based on MMSE and AFSA for TDRS system forward link.

This study mainly focuses on multiuser detection in tracking and data relay satellite (TDRS) system forward link. Minimum mean square error (MMSE) is a low complexity multiuser detection method, but MMSE detector cannot achieve satisfactory bit error ratio and near-far resistance, whereas artificial fish swarm algorithm (AFSA) is expert in optimization and it can realize the global convergence efficiently. Therefore, a hybrid multiuser detector based on MMSE and AFSA (MMSE-AFSA) is proposed in this paper. The result of MMSE and its modified formations are used as the initial values of artificial fishes to accelerate the speed of global convergence and reduce the iteration times for AFSA. The simulation results show that the bit error ratio and near-far resistance performances of the proposed detector are much better, compared with MF, DEC, and MMSE, and are quite close to OMD. Furthermore, the proposed MMSE-AFSA detector also has a large system capacity.

Design of pulse waveform for waveform division multiple access UWB wireless communication system.

A new multiple access scheme, Waveform Division Multiple Access (WDMA) based on the orthogonal wavelet function, is presented. After studying the correlation properties of different categories of single wavelet functions, the one with the best correlation property will be chosen as the foundation for combined waveform. In the communication system, each user is assigned to different combined orthogonal waveform. Demonstrated by simulation, combined waveform is more suitable than single wavelet function to be a communication medium in WDMA system. Due to the excellent orthogonality, the bit error rate (BER) of multiuser with combined waveforms is so close to that of single user in a synchronous system. That is to say, the multiple access interference (MAI) is almost eliminated. Furthermore, even in an asynchronous system without multiuser detection after matched filters, the result is still pretty ideal and satisfactory by using the third combination mode that will be mentioned in the study.

A multiuser detector based on artificial bee colony algorithm for DS-UWB systems.

Artificial Bee Colony (ABC) algorithm is an optimization algorithm based on the intelligent behavior of honey bee swarm. The ABC algorithm was developed to solve optimizing numerical problems and revealed premising results in processing time and solution quality. In ABC, a colony of artificial bees search for rich artificial food sources; the optimizing numerical problems are converted to the problem of finding the best parameter which minimizes an objective function. Then, the artificial bees randomly discover a population of initial solutions and then iteratively improve them by employing the behavior: moving towards better solutions by means of a neighbor search mechanism while abandoning poor solutions. In this paper, an efficient multiuser detector based on a suboptimal code mapping multiuser detector and artificial bee colony algorithm (SCM-ABC-MUD) is proposed and implemented in direct-sequence ultra-wideband (DS-UWB) systems under the additive white Gaussian noise (AWGN) channel. The simulation results demonstrate that the BER and the near-far effect resistance performances of this proposed algorithm are quite close to those of the optimum multiuser detector (OMD) while its computational complexity is much lower than that of OMD. Furthermore, the BER performance of SCM-ABC-MUD is not sensitive to the number of active users and can obtain a large system capacity.