Different Types Of Interference Research Articles

Portable electrochemical sensor for adrenaline detection using CoNi-MOF-based CS-PAM hydrogel

The development of a portable smartphone-based electrochemical sensor for analyzing adrenaline levels in real samples can make a great contribution to the research community worldwide. In order to achieve this goal, the key challenge is to build sensing interfaces with excellent electrocatalytic properties. In this work, microspherical bimetallic metal–organic frameworks (CoNi-MOF) consisting of nanoclusters were first synthesized using a hydrothermal method. On this basis, the catalytic activity of pure chitosan-polyacrylamide hydrogel (CS-PAM) was modulated by adding different amounts of CoNi-MOF during the in-situ synthesis of CS-PAM. Finally, a portable electrochemical detection system based on CS-PAM was established for the detection of adrenaline. A series of resulting composite hydrogels with a large specific surface area, abundant active sites, and unique network structure facilitate the enrichment and catalysis of adrenaline molecules. Under optimal conditions, the analytical platform constructed by using CoNi-MOF-based CS-PAM has the advantages of a wide detection range (0.5–10 and 10–2500 μM), a low detection limit (0.167 μM), and high sensitivity (0.182 and 0.133 μA·μM·cm−2). In addition, the sensor maintains selective detection of the target in the presence of many different types of interferences, and the current response is not significantly reduced even after 60 cycles of testing. We strongly believe that the designed smart portable sensing can realize the accurate determination of adrenaline in complex systems, and this study can provide new ideas for the research of MOFs-based hydrogels in electrochemical analysis.

Analysis and Protection of Electromagnetic Interference in the Industrial Field

With the continuous progress of science and technology, all kinds of electrical and electronic equipment are applied to production and life. However, bad electromagnetic interference will affect the normal operation of the equipment and reduce work efficiency. This paper analyzes the situation and source of electromagnetic interference in the industrial field and introduces several common anti-interference methods. And take corresponding measures according to different types of interference. This paper introduces electromagnetic shielding, filtering, and other technologies, and the application scenarios of various anti-interference methods are analyzed. When the working environment and the nature of interference emission change, the anti-interference strategy should be adjusted accordingly. Implementing these measures will significantly improve the electromagnetic compatibility of the system, reduce the electromagnetic interference phenomenon in the industrial site, and ensure the stable and efficient work of the system. Although electromagnetic interference cannot be eliminated, summarizing and analyzing existing technologies helps to develop new technologies and provides more possibilities for anti-interference.

An Automatic Frequency Control System for Transmission Machinery Based on Back-Propagation Neural Network Algorithm in the Internet of Things Environment

Abstract In order to reduce the error of automatic frequency control of transmission machinery under different disturbances, an automatic frequency control system of transmission machinery based on back-propagation (BP) neural network algorithm is designed. The gradient descent method and Newton method are combined to optimize the BP neural network. Aiming at the periodicity and trend of the actual mechanical vibration frequency time series, the first order backward difference is processed by the difference method, and the autoregressive sequence is derived to obtain the control model. The experimental results show that the system can realize the automatic control of transmission mechanical frequency with small error under different types of interference, and the control effect is ideal.

Uncovering the Limitations and Insights of Packet Status Prediction Models in IEEE 802.15.4-Based Wireless Networks and Insights from Data Science

Wireless networks play a pivotal role in various domains, including industrial automation, autonomous vehicles, robotics, and mobile sensor networks. This research investigates the critical issue of packet loss in modern wireless networks and aims to identify the conditions within a network’s environment that lead to such losses. We propose a packet status prediction model for data packets that travel through a wireless network based on the IEEE 802.15.4 standard and are exposed to five different types of interference in a controlled experimentation environment. The proposed model focuses on the packetization process and its impact on network robustness. This study explores the challenges posed by packet loss, particularly in the context of interference, and puts forth the hypothesis that specific environmental conditions are linked to packet loss occurrences. The contribution of this work lies in advancing our understanding of the conditions leading to packet loss in wireless networks. Data are retrieved with a single CC2531 USB Dongle Packet Sniffer, whose pieces of information on packets become the features of each packet from which the classifier model will gather the training data with the aim of predicting whether a packet will unsuccessfully arrive at its destination. We found that interference causes more packet loss than that caused by various devices using a WiFi communication protocol simultaneously. In addition, we found that the most important predictors are network strength and packet size; low network strength tends to lead to more packet loss, especially for larger packets. This study contributes to the ongoing efforts to predict and mitigate packet loss, emphasizing the need for adaptive models in dynamic wireless environments.

Interference management in 5G and beyond networks: A comprehensive survey

During the last decade, wireless data services have had an incredible impact on people’s lives in ways we could never have imagined. The number of mobile devices has increased exponentially and data traffic has almost doubled every year. Undoubtedly, the rate of growth will continue to be rapid with the explosive increase in demands for data rates, latency, massive connectivity, network reliability, and energy efficiency. In order to manage this level of growth and meet these requirements, the fifth-generation (5G) mobile communications network is envisioned as a revolutionary advancement combining various improvements to previous mobile generation networks and new technologies, including the use of millimeter wavebands (mm-wave), massive multiple-input multiple-output (mMIMO) multi-beam antennas, network densification, dynamic Time Division Duplex (TDD) transmission, and new waveforms with mixed numerologies. New revolutionary features including terahertz (THz) communications and the integration of Non-Terrestrial Networks (NTN) can further improve the performance and signal quality for future 6G networks. However, despite the inevitable benefits of all these key technologies, the heterogeneous and ultra-flexible structure of the 5G and beyond network brings non-orthogonality into the system and generates significant interference that needs to be handled carefully. Therefore, it is essential to design effective interference management schemes to mitigate severe and sometimes unpredictable interference in mobile networks. In this paper, we provide a comprehensive review of interference management in 5G and Beyond networks and discuss its future evolution. We start with a unified classification and a detailed explanation of the different types of interference and continue by presenting our taxonomy of existing interference management approaches. Then, after explaining interference measurement reports and signaling, we provide for each type of interference identified, an in-depth literature review and technical discussion of appropriate management schemes. We finish by discussing the main interference challenges that will be encountered in future 6G networks and by presenting insights on the suggested new interference management approaches, including useful guidelines for an AI-based solution. This review will provide a first-hand guide to the industry in determining the most relevant technology for interference management, and will also allow for consideration of future challenges and research directions.

Soil microbial diversity under different types of interference in birch secondary forest in the Greater Khingan Mountains in China.

Soil microorganisms are an important component of soil ecosystems with an indispensable role in forest ecosystems. We analyzed the soil microbial diversity in birch secondary forest formed by natural restoration or artificial reconstruction after interference by burning, clear cutting, and gradient cutting, and the Betula platyphylla Suk undisturbed forest in the Greater Khingan Mountains in China. Illumina high-throughput sequencing technology was used to analyze the characteristics of the soil microbial community during the restoration process of birch secondary forest caused by the different types of interference. The relationships between bacteria and fungi were analyzed. The gene functions of the soil bacterial community and the ecological functions of soil fungi were predicted using PICRUSt and FunGuild, respectively. At the phylum level, the species and quantity of bacteria were more abundant than that of fungi. At the genus level, no obvious differences in the abundance of bacteria were observed; there were obvious differences in the abundance of fungi. Among the eight sample plots, the artificial larch forest belt had the highest bacterial and fungal alpha diversity, which was slightly higher than undisturbed forest, while the other sample plots were significantly lower. Gradual cutting pure birch forest bacteria and fungi had the highest beta diversity, and artificial larch forest belt bacteria and heavy burn sample plot fungi had the lowest beta diversity. Samples from the cutting and burning sample plots were significantly different from the undisturbed forest at the phylum level of Acidobacteriae, Acidimicrobiia, Mortierellomycetes and Sordariomycetes. We found statistical differences in biomarkers between bacterial and fungal communities in undisturbed forest and artificial larch forest belt and burn sample plots. PICRUSt prediction and FunGuild prediction showed that soil bacterial and fungal communities were rich in gene and ecological functions, respectively. In the microbial network, the stability or anti-interference performance of the fungal community was higher than that of bacteria. Our data reveal the characteristics of the soil microbial community during the restoration process of Betula platyphylla Suk secondary forest under different types of disturbance, which is of great significance for understanding the role of soil microorganisms in the forest ecological cycle.

Modal Filter Based on a Microstrip Line With Two Side Conductors Grounded at Both Ends

This article presents a modal filter (MF) based on a microstrip line with two side conductors grounded at both ends. New analytical expressions for calculating the time response in such a structure are given. For their validation, an MF prototype was made and the transmission coefficient was measured in the frequency range from 0 to 12 GHz. In addition, the full-wave electrodynamic simulation and the analysis based on the transmission line theory were carried out. The MF frequency responses were converted into time domain responses in the Advanced Design System. At the MF output, the responses to an ultra-wideband pulse, a high-frequency damped sinusoid, and a sinusoid with Gaussian modulation were analyzed. The results for the analytical model are in good agreement with the results of measurements and simulations by the two approaches. It was experimentally proven that the structure under study provides the protection of electrical circuits from different types of interference by separating them in time and decreasing their effect on the components. Finally, signal integrity was evaluated for two data rates. It was found that the MF under investigation may be used in signal circuits up to 1 Gb/s.

Sum Rate Maximization and Consistency in D2D Communication Based on ACO and Game Theory

Cellular network is the most popular network setup among today’s wireless communication systems. The primary resource in a cellular system is the spectrum for communication, and owing to the rising number of cellular users, the spectrum that is currently accessible from different service providers is depleting quickly. The resource or channel allocation is the most hindering task in cellular networks. Many efforts have been taken by many researchers to allocate the resources properly in order to increase the channel utilization and it is found that one effective method for reusing the channels inside a cell is device to device (D2D) communication. D2D communication was first developed in order to achieve the fundamental goals of fast data rates, widespread coverage with little latency, energy efficiency, and low per-information transmission costs. The dynamic behaviour of this network set-up again increases the risk of different types of interferences, which is another issue faced by the researchers. In this paper an effort is taken to understand and solve various aspects of channel allocation and Cellular networks have incorporated interference management in D2D communication especially. The two major issues of allocation of resource and management of interference in D2D communication is addressed here. This paper considers the meta heuristic algorithm namely Ant Colony Optimization (ACO) for resource allocation issue and interference management. The sum rate maximization is achieved through Game theory along with the concept of resource exchange in turn to increase the consistency of D2D communication setup. The results demonstrate that our algorithm can significantly increase the sum rate of D2D pairs when compared to other algorithms suggested by related works.

Effects of different types of interference on nurses' working memory: An ERP study.

To explore the effects of different types of interference on nurses' working memory, and the role of attention control. A repeated measures design. A single-factor, four-level within-subjects design was adopted. Thirty-one nurses completed a delay-recognition task with four blocks in September 2020: Interrupting Stimulus (stimuli requiring attention), Distracting Stimulus (stimuli to-be-ignored), No Interference and Passively View. Behavioural responses of the participants and EEG data were recorded. MATLAB 21b and EEGLAB 21b were used for electroencephalogram data preprocessing and data extraction. Firstly, when nursing information system was used as task material, the accuracy rate and false alarm rate of primary tasks under interruption condition was statistically significantly different with that of distraction and no interference condition. There is a statistically significant difference in electroencephalogram measurement between correct and wrong response under interruption. Secondly, the role of attention control was different under interruption and distraction. There was a statistically significant positive correlation between the average amplitude distraction attention control index and task accuracy, and statistically significant negative correlation between the latency interruption attention control index and the accuracy of working memory task. There were different effects of interruptions and distractions on nurses' working memory and the role of attention control were also different. Measures can be designed according to these results to reduce the negative impact of interference on nurses, so as to improve work efficiency and reduce patient risk. This study has implications for clinical nursing during human-computer interaction. Resumption of the speed of the target information after an interruption affected task performance. Therefore, interventions should be designed to reduce the time needed for nurses to extract task information after an interruption, such as providing key clues in the information system interface. Registered nurses participated in the study as subjects.

Enhancing MIMO Spatial-Multiplexing and Parallel-Decoding under Interference by Computational Feedback

In this paper, we propose a new digital Hard-Successive-Interference-Cancellation (HSIC), the Alternating Projections-HSIC (AP-HSIC), an innovative fast computational feedback algorithm that deals with various destructive phenomena from different types of interferences. The correctness and convergence of the proposed algorithm are provided, and its complexity is given. The proposed algorithm possesses the functionality of canceling digital interference without the aid of physical feedback between the receiver and the transmitter or the loading of learning information about the state of the Multiple Input–Multiple Output (MIMO) channel to the transmitter. The proposed AP-HSIC algorithm enables a parallel decoding process from the parallel transmission of Orthogonal- Space–Time-Block-Coding (OSTBC) under a complex and challenging wireless environment to facilitate the Dynamic Spectrum Sharing (DSS) capability. We present a performance comparison of the proposed algorithm with the algorithm for Multi-Group-Space–Time-Coding (MGSTC) under MIMO fading channels and general interference or high-level Additive White Gaussian Noise (AWGN). Mathematical analysis and real-time simulations show the advantages of the proposed algorithm compared to the MGSTC decoding algorithm.

Laboratory Analysis on the Performance of 5G NSA Communication in a Suburban Scenario

The propagation of information by electromagnetic waves suffers different types of interference, according to the characteristics of the environment. The 5G system relies on adaptive modulation and coding techniques to better suit the channel and maximize effective data exchange between the user equipment and the network. Practical studies on the behaviour of the system under different environmental conditions, subject to attenuation processes such as fading, are important to understand and improve the 5G efficiency. This work has analysed the effect of the MCS (Modulation and Coding Scheme) variation on throughput for channel degraded by the multipath fading effect in a mobile communication. The analysis was carried out showing that the decision algorithms in terms of MCS switching to maintain adequate data rates according to the requirement (QoS) is an important factor. Considering both 64 QAM and 256 QAM, the throughput degradation effect was more evident in higher-order modulations due to the higher probability of bit error in the symbol constellation. This study can be a key for understanding and developing robust MCS switcher for 5G and beyond communications.

SMART: Vision-Based Method of Cooperative Surveillance and Tracking by Multiple UAVs in the Urban Environment

UAV surveillance and tracking have attracted great enthusiasm in intelligent transportation, and various approaches have been reported up to now. However, these approaches often ignored the uncertainties in the urban environment, such as occlusion, view change, and background clutter. Ignoring these uncertain factors often leads to a reduction in surveillance performance and tracking quality. This study devotes to improving the cooperative surveillance capability of multi-UAV formation by designing different cooperative strategies in the urban environment. To be specific, a novel cooperative architecture is designed to control the observation locations of multiple UAVs throughout the formation process. For different types of interference, we introduce a novel target recognition rate of each UAV as the decision factor and design corresponding cooperative strategies to guarantee the accuracy of cooperative surveillance. Based on this architecture, we develop a vision-based method of cooperative surveillance and tracking by multiple UAVs (SMART) whose objective function is the motion cost and flight reliability of UAVs to ensure that each UAV can be in the optimal surveillance location for the target. The proposed SMART skillfully integrates the strict, elastic, and flight constraint strategies. During the execution of the multi-UAV formation, the inherent safety constraints of multiple UAVs and the designed strategies are used to solve the quadratic optimization model to adjust the locations of these UAVs. To demonstrate the superiority of our method, we conduct a 3D simulation urban environment and devise several experiments to analyze the performance of SMART on it. The experimental results demonstrate that SMART can not only maintain the high cooperative flight capability, but also provide high flexibility and fault tolerance.

Oculomotor rehearsal in visuospatial working memory

The neural and cognitive mechanisms of spatial working memory are tightly coupled with the systems that control eye movements, but the precise nature of this coupling is not well understood. It has been argued that the oculomotor system is selectively involved in rehearsal of spatial but not visual material in visuospatial working memory. However, few studies have directly compared the effect of saccadic interference on visual and spatial memory, and there is little consensus on how the underlying working memory representation is affected by saccadic interference. In this study we aimed to examine how working memory for visual and spatial features is affected by overt and covert attentional interference across two experiments. Participants were shown a memory array, then asked to either maintain fixation or to overtly or covertly shift attention in a detection task during the delay period. Using the continuous report task we directly examined the precision of visual and spatial working memory representations and fit psychophysical functions to investigate the sources of recall error associated with different types of interference. These data were interpreted in terms of embodied theories of attention and memory and provide new insights into the nature of the interactions between cognitive and motor systems.

Adaptive variational mode extraction method for bearing fault diagnosis based on window fusion

Fault-related mode extraction and demodulation has become one of the most important and mature strategies to detect bearings fault. Variational mode extraction (VME) can extract a specific mode from a non-stationary signal without paying attention to other irrelevant modes. However, like variational mode decomposition method, the determination of some key parameters in advance is significant to fully demonstrate the capability of VME. Thus, in this study, an adaptive VME (AVME) method is proposed for bearing fault diagnosis. Firstly, different types of interference are employed to investigate the convergence characteristics of the mode center frequency. Then, to minimize the influence of interferences, an adaptive window fusion strategy based on log-cycligram and correlation coefficient is developed, from which the initial center frequency (ICF) and the balancing parameter can be determined. Finally, the superiority of the proposed method for fault diagnosis is proven by comparing it with some existing methods on the synthesized signal and experimental data.

A comparative study of single-channel signal processing methods in fetal phonocardiography.

Fetal phonocardiography is a non-invasive, completely passive and low-cost method based on sensing acoustic signals from the maternal abdomen. However, different types of interference are sensed along with the desired fetal phonocardiography. This study focuses on the comparison of fetal phonocardiography filtering using eight algorithms: Savitzky-Golay filter, finite impulse response filter, adaptive wavelet transform, maximal overlap discrete wavelet transform, variational mode decomposition, empirical mode decomposition, ensemble empirical mode decomposition, and complete ensemble empirical mode decomposition with adaptive noise. The effectiveness of those methods was tested on four types of interference (maternal sounds, movement artifacts, Gaussian noise, and ambient noise) and eleven combinations of these disturbances. The dataset was created using two synthetic records r01 and r02, where the record r02 was loaded with higher levels of interference than the record r01. The evaluation was performed using the objective parameters such as accuracy of the detection of S1 and S2 sounds, signal-to-noise ratio improvement, and mean error of heart interval measurement. According to all parameters, the best results were achieved using the complete ensemble empirical mode decomposition with adaptive noise method with average values of accuracy = 91.53% in the detection of S1 and accuracy = 68.89% in the detection of S2. The average value of signal-to-noise ratio improvement achieved by complete ensemble empirical mode decomposition with adaptive noise method was 9.75 dB and the average value of the mean error of heart interval measurement was 3.27 ms.

Modeling Cellular Network Infrastructure in SUMO

Communication networks are becoming an increasingly important part of the mobility system. They allow traffic participants to be connected and to exchange information related to traffic and roads. The information exchange impacts the behavior of traffic participants, such as the selection of travel routes or their mobility dynamics. Considering infrastructure-based networks, the information exchange depends on the availability of the network infrastructure and the quality of the communication links. Specifically in urban areas, today’s 4G and 5G networks deploy small cells of high capacity, which do not provide ubiquitous cellular coverage due to their small range, signal blocking, etc. Therefore, the accurate modeling of the network infrastructure and its integration in simulation scenarios in microscopic traffic simulation software is gaining relevance. Unlike traffic infrastructure, such as traffic lights, the simulation of a cellular network infrastructure is not natively supported in SUMO. Instead, the protocols, functions and entities of the communication system with the physical wireless transmission are modeled in a dedicated and specialized network simulator that is coupled with SUMO. The disadvantage of this approach is that the simulated SUMO entities, typically vehicles, are not aware which portions of the roads are covered by wireless cells and what quality the wireless communication links have. In this paper, we propose a method for modeling the cellular infrastructure in SUMO that introduces a cellular coverage layer to SUMO. This layer models cell sites in a regular hexagonal grid, where each site is served by a base station. Following commonly accepted guidelines for the evaluation of cellular communication system, the method facilitates standardized and realistic modeling of the cellular coverage, including cell sites, antenna characteristics, cell association and handover. In order to ease the applicability of the method, we describe the work flow to create cell sites. As a representative case, we have applied the method to InTAS, the SUMO Ingolstadt traffic scenario and applied real data for the cellular infrastructure. We validate the approach by simulating a Cellular V2X system with sidelink connectivity in an urban macro cell environment by coupling SUMO enhanced by the proposed connectivity sublayer with ARTERY-C, a network simulator for Cellular V2X. As a proof-of-concept, we present a signal-to-interference noise ratio (SINR) coverage map and further evaluate the impact of different types of interference. We also demonstrate the effect of advanced features of cellular networks such as inter-cell interference coordination (ICIC) and sidelink communication modes of Cellular V2X with dynamic switching between the in-coverage and out-of-coverage mode.

Training a Resilient Q-network against Observational Interference

Deep reinforcement learning (DRL) has demonstrated impressive performance in various gaming simulators and real-world applications. In practice, however, a DRL agent may receive faulty observation by abrupt interferences such as black-out, frozen-screen, and adversarial perturbation. How to design a resilient DRL algorithm against these rare but mission-critical and safety-crucial scenarios is an essential yet challenging task. In this paper, we consider a deep q-network (DQN) framework training with an auxiliary task of observational interferences such as artificial noises. Inspired by causal inference for observational interference, we propose a causal inference based DQN algorithm called causal inference Q-network (CIQ). We evaluate the performance of CIQ in several benchmark DQN environments with different types of interferences as auxiliary labels. Our experimental results show that the proposed CIQ method could achieve higher performance and more resilience against observational interferences.

Variability of Intraoperative Electrostimulation Parameters in Conscious Individuals: Language Fasciculi

The authors analyzed the current-intensity thresholds for electrostimulation of language fasciculi and the possible consequences of threshold variability on brain mapping. A prospective protocol of subcortical electrostimulation was used in 50 patients undergoing brain mapping, directly stimulating presumed language fasciculi identified by diffusion tensor imaging. The stimulation-intensity thresholds for identification of language fasciculi varied among patients (mean minimum current intensity of 4.4mA, range= 1.5-10mA, standard deviation= 1.1mA), and 23% of fascicular interferences were detected only above 5mA. Repeated stimulation of the same site with the same intensity led to different types of interferences in 20% of patients, and a higher current intensity led to changes in the type of response in 27%. The mean minimum stimulation intensities did not differ significantly between different fasciculi, between the different types of interference obtained, or with age, sex, or type of tumor. Positive results on cortical mapping were significantly associated with positive results on subcortical mapping (P<0.001). Subcortical intensity thresholds were slightly lower than cortical ones (mean= 4.43 vs. 5.25mA, P=0.034). In 23 of 50 subcortical mappings, fascicular stimulation produced no language interference. Individual variability of minimum stimulation-intensity thresholds for identification of language fasciculi is frequent. Nevertheless, even when a high current intensity was used, many stimulations on language fasciculi remained negative for various hypothetic reasons. Finding the optimal current intensity for identifying language fasciculi is of paramount importance to refine the clinical results and scientific data derived from brain mapping.

How bilingualism modulates selective attention in children

There is substantial evidence that learning and using multiple languages modulates selective attention in children. The current study investigated the mechanisms that drive this modification. Specifically, we asked whether the need for constant management of competing languages in bilinguals increases attentional capacity, or draws on the available resources such that they need to be economised to support optimal task performance. Monolingual and bilingual children aged 7–12 attended to a narrative presented in one ear, while ignoring different types of interference in the other ear. We used EEG to capture the neural encoding of attended and unattended speech envelopes, and assess how well they can be reconstructed from the responses of the neuronal populations that encode them. Despite equivalent behavioral performance, monolingual and bilingual children encoded attended speech differently, with the pattern of encoding across conditions in bilinguals suggesting a redistribution of the available attentional capacity, rather than its enhancement.

A Sparse Reconstruction Domain Transfer Method for Interference Suppression in Artificial Olfactory System

In recent years, the electronic nose (E-nose) technology has made a great progress in practical application. However, the interferences, such as the interferences from non-target gas, manufacturing deviation and sensor drift, have a negative impact on the detection accuracy during the real-time detection process, which will affect the industrialization and commercialization of E-nose. Thus, it is necessary to research the method of interference suppression for realizing the long-term and stable detection. This paper proposes a sparse reconstruction domain transfer (SRDT) method to suppress the interference in E-nose. The proposed method is to find the optimal subspace for each dataset, and sparsely reconstruct the transformed data after transforming to the optimal subspace, which can realize the knowledge transfer in the data domains with and without interference information. Three different datasets of E-nose are used to verify the effectiveness of the proposed method, including the benchmark sensor drift data, the dataset collected by three identical sets of E-nose systems and the dataset of background interference caused by carrier gas. The average recognition rate is 92.97% in the first dataset. The recognition rate is 93.75% and 90.27% separately for the two tasks of the second dataset, and 85.16% and 84.62% separately for the two tasks of the third dataset. The experiment results show that different types of interference in E-nose can be satisfactorily suppressed.