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As one of major types of microplastics (MPs), microfibers (MFs) are widely found in the marine ecosystem and can induce diverse impacts on various marine organisms. Sedentary species, such as mussels, can act as bioindicators for monitoring marine contamination. Hence, in this study, we used mussels (Mytilus galloprovincialis) to examine the toxicity of polyethylene terephthalate (PET) MFs of 100 μm size at concentrations of 0.0005, 0.1, 1, 10, and 100 mg/L for 32 days. PET MFs accumulated only in the stomachs and intestines of the mussels and caused digestive tubule atrophy. After exposure to PET MFs, no alteration in the mortality rate, shell height, length, and weight of the mussels was observed. However, the gonadal index decreased with increasing concentrations of PET MFs. This is because PET MFs decrease the sex hormones estradiol and testosterone in mussels, even at environmentally relevant concentrations. Furthermore, chronic exposure to PET MFs increased the activities of antioxidant-related (catalase and superoxide dismutase) and neurotoxicity-related (acetylcholine esterase) enzymes in the digestive gland and gill tissues of mussels. In addition, cellular immune parameters of apoptosis and DNA damage were observed in mussel hemocytes. Thus, this study demonstrates the risks of MPs in real marine environments by assessing how long-term exposure to low concentrations of PET MFs can cause potential sublethal impacts and reproductive failure in mussels.

BACKGROUND Spatial navigation impairment is recognized as a potential marker of preclinical dementia. Virtual reality (VR) technology has been introduced as a promising framework for dementia research. Recent work has demonstrated further possibilities that integrating electroencephalography (EEG) sensors into VR headsets can provide invaluable information relevant to cognitive impairment. OBJECTIVE The aim of this study is to explore the feasibility of spatial navigation assessment in EEG-integrated VR devices for assessing cognitive impairment. This study will further investigate the neurophysiological characteristics of wearable EEG sensors during a VR spatial navigation game. METHODS Participants recruited for the study will be grouped into three based on their cognitive status (mild Alzheimer’s disease group, mild cognitive impairment group, and healthy controls). The spatial navigation game is designed based on the hidden goal task to assess egocentric and allocentric spatial information processing. As outcome measures, spatial navigation errors, spectral density characteristics of EEG signals, and behavioral responses in VR will be analyzed. RESULTS A spatial navigation game for VR was developed in March 2021. Recruitment commenced in June 2021. CONCLUSIONS The results of this study will provide a comprehensive understanding of VR-based spatial navigation assessment, in terms of cognitive measurement including early screening. If positive, our approach based on EEG-integrated VR devices could constitute an effective cognitive impairment screening test tool that can provide brain dynamic characteristics as well as information on spatial navigation deficits.

Many studies have investigated the toxic effects of microplastics in marine organisms, but most studied nano-sized round microplastics at high concentrations and were not environmentally relevant. To understand the cellular toxicity of polyethylene terephthalate microfibers (PET-MFs) by length (50 and 100 μm), Mediterranean mussels (Mytilus galloprovincialis) were exposed to environmental (0.5 μg/L) and high (100 mg/L) MF concentrations for four days. Short PET-MFs accumulated in the lower intestinal organs of the mussels, but long PET-MFs were only observed in the upper intestinal organs. Both sized PET-MFs affected necrosis, DNA damage, reactive oxygen species, nitric oxide, and acetylcholinesterase (AChE) activity. Significant MF length-dependent effects occurred at environmentally relevant concentrations for DNA damage (100 μm MFs) and AChE activity (50 μm MFs). However, length effects disappeared at the higher exposure concentration. The current study provides potentially sensitive indicators to detect MFs exposure and the ecotoxicological implications of MFs in marine ecosystems.

Motion control systems require an isochronal real-time guarantee that each control task should periodically produce outputs with no jitters. However, it is difficult to build up such a tight isochronal system with a multicore architecture and a general-purpose operating system, because the inherent resource sharing principle leads to large jitters to the control tasks. This paper proposes a software pipelining framework for an EtherCAT-based motion controller that achieves a tight isochronal guarantee with that combination. The tight guarantee is possible by multicore partitioning and reservation-aware task phasing, which reduce resource contentions between the tasks on each stage of the pipeline. Through experiments, we show that the proposed pipelining framework gives a tight isochronal guarantee with high scalability in terms of the number of motion transactions. On a real 8-axis motion control platform with two processor cores dedicated to the pipeline and a slight modification of the Linux operating system, it achieves a maximum jitter of $10\ \upmu\text{s} $ for four motion transactions with a common period of 1.6 ms, whereas a priority-driven method gives a maximum jitter of a few hundreds of microsecond under the same condition.

The Life Log records computer use, details of mobile device use and sensor values collected from user's activities. Data recorded in life log is analyzed by the cloud computing and turned into useful information. Many researches to provide useful services to human beings making use of the useful information have been widely conducted. This paper surveys life log and proposes a record-preventing method and a device that is facilitated with the record-preventing function. The proposed system consists of the input component, the control component, and the output component. The input component reads data from the GPS receiver, the RFID (Radio-Frequency Identification) reader, the QR code (Quick Response Code) reader, a camera, and sensors. The control component compares the input data with the data stored in the database in order to determine whether the user is within a forbidden area. The control component stops life-logging if the user is within a forbidden area.

As SSDs are increasingly being used as high-performance storage or caches, attention is increasingly paid to the provision of SSDs with Quality-of-Service for I/O request streams of various applications in server systems. Since most SSDs are using the AHCI controller interface on a SATA bus, it is not possible to provide a differentiated service by distinguishing each I/O stream from others within the SSD. However, since a new SSD interface, the NVME controller interface on a PCI Express bus, has been proposed, it is now possible to recognize each I/O stream and schedule I/O requests within the SSD for differentiated services. This paper proposes Flash Operation Group Scheduling within NVME-based flash storage devices, and demonstrates through QEMU-based simulation that we can achieve a proportional bandwidth share for each I/O stream.

In this paper, we present MQMAI, multi-queue command-based data transfer mechanism that enables efficient and scalable access to multiple accelerators on FPGA via PCIe. The proposed framework provides a user-friendly non-blocking API with enhanced I/O parallelism to access FPGA-based accelerators. MQMAI is composed of I/O software stack in host CPU and accelerator controller in FPGA. A round-robin policy is developed to allow efficient run-time access to shared accelerators requested through multiple threads/applications. Our experimental results show that our proposed framework reduces the total latency of multi accelerators access significantly compared to the current state-of-the-art framework, RIFFA, by 18x for 4 threads per accelerator access.

Estimation techniques such as Unscented Kalman filter (UKF) are deployed for accurate joint location estimation in cooperative localization of cyber physical systems (CPS), e.g., to locate each robot in a cooperative mobile robotic network in GPS-denied environments. In order to avoid single point of failure in the centralized implementation of such estimation techniques, the decentralization of estimation algorithms has attracted considerable attention in the past two decades. However, the design of decentralized algorithms with reduced communication cost without loss in accuracy for compute-intensive estimation techniques such as UKF has been challenging. In the decentralized UKF, the tasks are partitioned and computed locally at robot nodes. Data communication overhead is overwhelming due to tight data dependency between the robots' computations. In this paper, we present a CPS framework for UKF decentralization in which computation and communication are tightly intertwined and computation replication is deployed in order to reduce the communication overhead among cooperative mobile robots. We demonstrate and evaluate the performance of our proposed work in a wireless network of 15 Raspberry Pi 3 B, with quad-core 1.2GHz 64bit CPU, emulating a network of mobile robots with onboard computation and communication capabilities. Our experimental results show that the End-to-End execution time of decentralized UKF prediction and update steps with replication are faster by up to 12.29 and 3.57 times, respectively, compared to the partially decentralized UKF algorithm of [1].

In this paper, we proposed an information fusion method for enhancement of automatic dependent surveillance - broadcast (ADS-B) system which is one of the next generation navigation system. Although ADS-B provides better performance than traditional radar, ADS-B still has error due to dependence of global navigation satellite system (GNSS) information. In this paper, we improved the ADS-B performance using information fusion of multilateration (MLAT) and wide area multilateration (WAM). Information fusion provides accurate data compared to original data. Mostly, information fusion methods use Kalman filter or IMM(interacting multiple model) filter as a subfilter. However, we used Robust IMM filter as a subfilter to improve the aircraft tracking performance. Also, we use actual ADS-B data not virtual data to increase reliability of our information fusion method.