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The carrier is one of the key components used in wastewater treatment, which can enrich microorganisms at the surface to improve the amount of biomass in the reactor. Monitoring and adjusting the number of carriers is a key component for the processing efficiency of the ecosystem, which directly impacts wastewater treatment effectiveness. Therefore, carrier detection in wastewater microscopic images is important in the urban domestic wastewater treatment process. The current process to detect carriers is operator-dependent, which is time-consuming and expensive. Though a number of general object or cell detection approaches are used for this task, their effectiveness is limited because the carrier and background are similar and there are defective carriers as background noise. In this paper, we propose CarrDet, the first deep learning-based carrier detection framework for wastewater treatment. CarrDet uses a carrier feature block attention module and a symmetry-based defective carrier detection module to detect carriers with shallow edges and reduce false positives caused by defective carriers, respectively. To evaluate CarrDet, we propose a carrier dataset of 600 wastewater microscopic images, manually annotated by experts. Compared with state-of-the-art object detection methods, CarrDet shows superior performance in terms of both accuracy and speed, achieving a mAP of 94.32 and an IPS of 4.93. We employed CarrDet to confirm the detection results of 621 wastewater microscopic images, which were detected by inexperienced engineers who are new to the field. CarrDet added 398 unrecognized carriers with shallow edges and corrected 273 incorrect manual annotations in 5 min, which emphasizes the efficiency and practicality of CarrDet for practical business scenarios.

IntroductionExisting evidence suggests an association between certain vitamins and metabolic syndrome (MetS), but few epidemiological studies have focused on the effects of multivitamin co-exposure on MetS. This study aims to investigate the associations of the individual or multiple water-soluble vitamins (i.e., vitamin C (VC), vitamin B9 (VB9), and vitamin B12 (VB12)) with co-exposure to MetS, as well as the dose-response relationships among them.MethodsA cross-sectional study was conducted by employing the National Health and Examination Surveys (NHANESs) 2003-2006. Multivariate-adjusted logistic regression models were used to explore the association between individual serum water-soluble vitamins and the risk of MetS and its components, including waist circumference, triglyceride, high-density lipoprotein, blood pressure, and fasting plasma glucose. Restricted cubic splines were performed to explore the dose-response relationships among them. The quantile g-computation method was adopted to explore the associations of multiple water-soluble vitamins co-exposure with MetS risk and MetS components.ResultsA total of 8983 subjects were involved in the study, of whom 1443 were diagnosed with MetS. The MetS groups had a higher proportion of participants with age ≥60 years, BMI ≥30 kg/m2, and insufficient physical activity. Compared with the lowest quartile, the third (OR=0.67, 95% CI: 0.48, 0.94) and highest quartiles (OR=0.52, 95%CI: 0.35, 0.76) of VC were associated with lower MetS risk. Restricted cubic splines showed negative dose-response relationships among VC, VB9 and VB12, and MetS. Regarding MetS components, higher VC quartiles were associated with lower waist circumference, triglyceride, blood pressure, and fasting plasma glucose, while higher VC and VB9 quartiles were associated with higher high-density lipoprotein (HDL). Co-exposure to VC, VB9, and VB12 was significantly inversely associated with MetS, with ORs (95% CI) of 0.81 (0.74, 0.89) and 0.84 (0.78, 0.90) in the conditional and marginal structural models, respectively. Furthermore, we found that VC, VB9, and VB12 co-exposure were negatively associated with waist circumference and blood pressure, while VC, VB9, and VB12 co-exposure were positively associated with HDL.ConclusionThis study revealed negative associations of VC, VB9, and VB12 with MetS, while the high water-soluble vitamin co-exposure was associated with a lower MetS risk.

Due to the material variations of lithium-ion cells and fluctuations in their manufacturing precision, differences exist in electrochemical characteristics of cells, which inevitably lead to a reduction in the available capacity and premature failure of a battery pack with multiple cells configured in series, parallel, and series–parallel. Screening cells that have similar electrochemical characteristics to overcome the inconsistency among cells in a battery pack is a challenging problem. This paper proposes an approach for lithium -ion cell screening using convolutional neural networks (CNNs) based on two-step time-series clustering (TTSC) and hybrid resampling for imbalanced data, which takes into account the dynamic characteristics of lithium-ion cells, thus ensuring that the screened cells have similar electrochemical characteristics. In this approach, we propose the TTSC to label the raw samples and propose the hybrid resampling method to solve the sample imbalance issue, thereby obtaining labeled and balanced datasets and establishing the CNN model for online cell screening. Finally, industrial applications verify the effectiveness of the proposed approach and the inconsistency rate of the screened cells drops by 91.08%.

An Optical Burst Switching (OBS) network is vulnerable to Burst Header Packet (BHP) flooding attack. In flooding attacks, edge nodes send BHPs at a high rate to reserve bandwidth for unrealized data bursts, which leads to a waste of bandwidth, a decrease in network performance, and massive data loss. Machine learning techniques are utilized to detect this attack in the OBS network. In this paper, we propose a particle swarm optimization–support vector machine (PSO-SVM) model for detecting BHP flooding attacks, in which the PSO is used to optimize the parameters of the SVM. We use the dataset provided by the UCI warehouse to train and test the model. The experimental results show that the detection accuracy of the PSO-SVM model reaches 95.0%, which is 9.4%, 9.6%, 20.7%, 8% higher than naïve Bayes, SVM, k-nearest neighbor, and decision tree. Although DCNN outperforms our model, it requires more processing and training time. Collectively, our approach is effective and high-efficiency in detecting flooding attacks in optical burst switching networks and maintaining network stability and security.

Improving spectral efficiency under a certain energy limitation is an important design metric for future wireless communications as a response to the growing transmission demand of wireless devices. In order to improve spectral efficiency for communication systems without increasing energy consumption, this paper considers a non-orthogonal multiple access (NOMA)–based cognitive radio network, with the assistance of a wireless-powered relay station (RS), and then analyzes the system outage performance under amplified-and-forward (AF) and decoded-and-forward (DF) cooperative transmission modes. Specifically, the base station (BS) has the opportunity to cooperate by transmitting information through the RS, depending on whether the RS can harvest sufficient RF energy for cooperative transmission. That is to say, when the energy stored by the RS is sufficient for cooperative transmission, the RS will assist the BS to forward information; otherwise, the BS will send information through direct links, while the RS converts the radio frequency (RF) signals sent by the BS into energy for future transmission. Moreover, the transmission power required by the RS for cooperative transmission is usually relatively large, while the amount of harvested energy by the RS in a transmission slot is usually low, so it takes several consecutive time slots to accumulate enough transmission energy. To this end, we utilize a discrete-time Markov chain to describe the processes of charging and discharging of the RS. Subsequently, we derive the closed-form outage probabilities of both the primary and secondary systems for the considered system in AF and DF modes through mathematical analysis, and verify the accuracy of the analyses through Monte Carlo simulation. The simulation results show that the two proposed cooperative transmission schemes with AF and DF relaying techniques outperform both direct transmission and other similar schemes in both the primary and secondary system, while the DF scheme can provide better performance than the AF scheme within the range of setting values.

Time-triggered systems play an important role in industrial embedded systems. The time-triggered network is deployed on the time-triggered network-on-chip implementation. It ensures the safety-critical industrial communication for real-time embedded multiprocessor systems. To guarantee the safety-critical requirements for communication, each message is transmitted by a predefined static schedule. However, synthesizing a feasible schedule is a challenge because both spatial and temporal constraints should be considered. This article presents a novel memetic-based schedule synthesis algorithm to derive a feasible schedule by determining the offset of messages on the time-triggered network-on-chip. Memetic-based schedule synthesis algorithm is based on memetic algorithm, which incorporates local search in the iterations of general genetic algorithm. We compare memetic-based schedule synthesis algorithm with genetic algorithm in different scale of time-triggered network-on-chip and number of messages. The experimental results show that the memetic-based schedule synthesis algorithm is effective to synthesize a feasible schedule, and the failure schedule synthesized by memetic-based schedule synthesis algorithm is only 34.2% in average compared to the conventional genetic algorithm.

The vehicular participatory sensing network (VPSN) is now becoming more and more prevalent, and additionally has shown its great potential in various applications. A general VPSN consists of many tasks from task, publishers, trading platforms and a crowd of participants. Some literature treats publishers and the trading platform as a whole, which is impractical since they are two independent economic entities with respective purposes. For a trading platform in markets, its purpose is to maximize the profit by selecting tasks and recruiting participants who satisfy the requirements of accepted tasks, rather than to improve the quality of each task. This scheduling problem for a trading platform consists of two parts: which tasks should be selected and which participants to be recruited? In this paper, we investigate the scheduling problem in vehicular participatory sensing with the predictable mobility of each vehicle. A genetic-based trading scheduling algorithm (GTSA) is proposed to solve the scheduling problem. Experiments with a realistic dataset of taxi trajectories demonstrate that GTSA algorithm is efficient for trading platforms to gain considerable profit in VPSN.