Traditional Approaches Research Articles

Evolutionary Stress Detection Framework through Machine Learning and IoT (MLIoT-ESD)

Background: Life nowadays is full of stress due to lifestyle changes and the modernera race. Almost everyone around us is suffering from stress and anxiety. Mostly, stress identification is done by medical practitioners in a very late stage in which suitable help measures cannot be provided and hence result in suicides or early age deaths due to cardiac arrest, etc. One major reason behind the delay is the time required in stress identification by traditional approaches, and above that, the amount of time and financial support expected is always not feasible to be available. Hence, in this paper, we proposed an evolutionary research framework for stress identification by the usage of both machine learning and IoT. Here, we also conducted a pilot study on 83 records available over the decade since 2014 using PRISMA guidelines, and a bibliographic network visualization was also performed using VOS viewer. Objectives: This study aimed to develop a stress detection framework using Machine Learning and the Internet of Things (IoT) as technology advanced over a decade. Methods: More than 80 research papers from honorable repositories like Scopus and Web of Science were gathered according to the guidelines of PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) 2020, and the VOSviewer tool was further applied to construct the bibliographic depictions. Various datasets and methods used over ten years with their performance were also discussed. Results: This research was conducted to gather various types of stressors, the impact of various Machine Learning and IoT algorithms and concepts on various datasets and their respective results. Conclusion: Various available datasets and results with multiple algorithms were discussed in a crisp tabular form for better understanding. A methodology based on an amalgamation of Machine Learning and IoT was also proposed due to various research gaps available so that stress detection could be done in a cost-effective way.

A Review on Nano/Microfluidic Devices for Cell Isolation Techniques: Recent Progress and Advances

Abstract: Micro/nanofluidic devices and systems have gained increasing interest in healthcare applications over the last few decades because of their low cost and ease of customization, with only a small volume of sample fluid required. Many biological queries are now being addressed using various types of single-molecule research. With this rapid rise, the disadvantages of these methods are also becoming obvious. Micro/nanofluidics-based biochemical analysis outperforms traditional approaches in terms of sample volume, turnaround time, ease of operation, and processing efficiency. A complex and multifunctional micro/nanofluidic platform may be used for single-cell manipulation, treatment, detection, and sequencing. We present an overview of the current advances in micro/nanofluidic technology for single-cell research, focusing on cell capture, treatment, and biochemical analyses. The promise of single-cell analysis using micro/nanofluidics is also highlighted.

Interpretable Model-Driven Deep Network for Hyperspectral, Multispectral, and Panchromatic Image Fusion.

Simultaneously fusing hyperspectral (HS), multispectral (MS), and panchromatic (PAN) images brings a new paradigm to generate a high-resolution HS (HRHS) image. In this study, we propose an interpretable model-driven deep network for HS, MS, and PAN image fusion, called HMPNet. We first propose a new fusion model that utilizes a deep before describing the complicated relationship between the HRHS and PAN images owing to their large resolution difference. Consequently, the difficulty of traditional model-based approaches in designing suitable hand-crafted priors can be alleviated because this deep prior is learned from data. We further solve the optimization problem of this fusion model based on the proximal gradient descent (PGD) algorithm, achieved by a series of iterative steps. By unrolling these iterative steps into several network modules, we finally obtain the HMPNet. Therefore, all parameters besides the deep prior are learned in the deep network, simplifying the selection of optimal parameters in the fusion and achieving a favorable equilibrium between the spatial and spectral qualities. Meanwhile, all modules contained in the HMPNet have explainable physical meanings, which can improve its generalization capability. In the experiment, we exhibit the advantages of the HMPNet over other state-of-the-art methods from the aspects of visual comparison and quantitative analysis, where a series of simulated as well as real datasets are utilized for validation.

Effects of Situated Game Teaching Through Set Plays on Badminton Tactical Knowledge, Technical Ability, and Game Performance Among Turkish Secondary School Students

Background: The Situated Game Teaching Through Set Plays (SGTSP) model, a newly proposed curricular model, extends the previous game-based approaches by adopting the Theory of Situated Learning as a framework to focus on the relational and situational nature of the changing relations of all game parameters in a specific momentary game scenario. However, as a new game-based model, SGTSP warrants further empirical research investigations to establish its effectiveness at advancing student learning such as skill acquisition and game competence. Purpose: To examine the effects of the SGTSP curricular model on secondary school students’ tactical knowledge, technical ability, and game performance in badminton when compared with a technique-focused approach during a 10-lesson unit. Methods: A completely randomized block design with a repeated measure was used to address the research purpose. A sample of 158 sixth graders from eight classes at two Turkish middle schools (67 girls and 89 boys; Mage = 12.62 ± 0.47) were assigned to either the SGTSP (n = 79 from four classes) or the technique-focused approach conditions (n = 79 from four classes) for the experiment. Assessments were conducted before and after the badminton instructional unit. The statistical models consisted of one of the four technical ability performances, components of game performance, or tactical knowledge as a dependent variable and eight independent variables: teacher, treatment, class nested within teacher and treatment, gender, skill levels, and three 2-way interactions between treatment, gender, and skill levels. A series of repeated-measure multivariate analyses of variance were conducted to analyze the data. Results: Participants in both groups improved tactical knowledge, forehand long serve, forehand clear, smash, and drop skills, and game performances from pre to post. Participants in the SGTSP condition showed significantly greater improvements (). Discussion and Conclusion: The findings of this study substantiated the positive effects of the SGTSP model on developing secondary school students’ badminton tactical knowledge, technical ability, and game performances in badminton compared with the traditional technique-focused approach.

STNMDA: A Novel Model for Predicting Potential Microbe-Drug Associations with Structure-Aware Transformer

Introduction: Microbes are intimately involved in the physiological and pathological processes of numerous diseases. There is a critical need for new drugs to combat microbe-induced diseases in clinical settings. Predicting potential microbe-drug associations is, therefore, essential for both disease treatment and novel drug discovery. However, it is costly and time-consuming to verify these relationships through traditional wet lab approaches. Methods: We proposed an efficient computational model, STNMDA, that integrated a StructureAware Transformer (SAT) with a Deep Neural Network (DNN) classifier to infer latent microbedrug associations. The STNMDA began with a “random walk with a restart” approach to construct a heterogeneous network using Gaussian kernel similarity and functional similarity measures for microorganisms and drugs. This heterogeneous network was then fed into the SAT to extract attribute features and graph structures for each drug and microbe node. Finally, the DNN classifier calculated the probability of associations between microbes and drugs. Results: Extensive experimental results showed that STNMDA surpassed existing state-of-the-art models in performance on the MDAD and aBiofilm databases. In addition, the feasibility of STNMDA in confirming associations between microbes and drugs was demonstrated through case validations. Conclusion: Hence, STNMDA showed promise as a valuable tool for future prediction of microbedrug associations.

Toward Explainable Multiparty Learning: A Contrastive Knowledge Sharing Framework.

Multiparty learning provides solutions for training joint models with decentralized data under legal and practical constraints. However, traditional multiparty learning approaches are confronted with obstacles, such as system heterogeneity, statistical heterogeneity, and incentive design. Determining how to deal with these challenges and further improve the efficiency and performance of multiparty learning has become an urgent problem to be solved. In this article, we propose a novel contrastive multiparty learning framework for knowledge refinement and sharing with an accountable incentive mechanism. Since the existing parameter averaging method is contradictory to the learning paradigm of neural networks, we simulate the process of human cognition and communication and analogize multiparty learning as a many-to-one knowledge-sharing problem. The approach is capable of integrating the acquired explicit knowledge of each client in a transparent manner without privacy disclosure, and it reduces the dependence on data distribution and communication environments. The proposed scheme achieves significant improvement in model performance in a variety of scenarios, as we demonstrated through experiments on several real-world datasets.

Continuity and Evolution in Vermeule on Legal Interpretation

Abstract This article is a constructive interpretation of Adrian Vermeule’s thought on legal interpretation, putting his previous work on these issues in conversation with his explicit embrace of the classical legal tradition so prominently on display in Common Good Constitutionalism. What emerges is a picture of Vermeule’s contemporary thinking on interpretation that has an interesting core of continuity, one running concurrently with several significant conceptual and normative evolutions brought about by his more explicit embrace of a classical legal framework. Part I offers a synthesis of Vermeule’s treatment of core questions of legal interpretation pre-Common Good Constitutionalism. Part II begins by outlining Vermeule’s express turn to the classical legal tradition and how this tradition approaches important public law issues linked to interpretation. Part II then proceeds to pick out and elaborate on themes of continuity and evolution. I argue here that while Vermeule’s embrace of the classical legal tradition has involved several significant conceptual and normative shifts in his thinking, this has not involved repudiation of his prior work on legal interpretation. Rather, the thrust and insights of much of his previous public law thought on interpretation retain vibrancy within the explicitly classical framework he has now adopted. Overall, I suggest Common Good Constitutionalism, and the works accompanying and building upon it, can be profitably read as providing a form of retroactive coherence to Vermeule’s long-standing arguments concerning legal interpretation, offering them a unifying conceptual framework. From the perspective of a classical natural law jurist at least, Vermeule’s embrace of the classical tradition provides his extensive body of scholarship in this area a more explicit and robust conceptual and normative anchor.

Human-in-the-Loop Behavior Modeling via an Integral Concurrent Adaptive Inverse Reinforcement Learning.

One goal of artificial intelligence (AI) research is to teach machines how to learn from humans, such that they can perform a certain task in a natural human-like way. In this article, an online adaptive inverse reinforcement learning (IRL) approach to human behavior modeling is proposed to enhance machine intelligence for a class of linear human-in-the-loop (HiTL) systems using the state data only, where the human behavior is described by a linear quadratic optimal control model with an unknown weighting matrix for the quadratic cost function. First, an integral concurrent adaptive law is developed to learn the human feedback gain matrix online using the demonstrated state data only, which removes the persistent excitation (PE) conditions required by traditional adaptive estimation approaches and thus is more in line with real applications. Then, with the learned feedback gain matrix, the IRL problem is formulated as a linear matrix inequality (LMI) optimization problem, which can be efficiently solved to retrieve the weighting matrix of the human cost function. Finally, a simulation example is provided to illustrate the effectiveness of the proposed approach.

Current Development of Data Resources and Bioinformatics Tools for Anticoronavirus Peptide.

Since December 2019, the emergence of severe acute respiratory syndrome coronavirus 2, which gave rise to coronavirus disease 2019 (COVID-19), has considerably impacted global health. The identification of effective anticoronavirus peptides (ACVPs) and the establishment of robust data storage methods are critical in the fight against COVID-19. Traditional wet-lab peptide discovery approaches are timeconsuming and labor-intensive. With advancements in computer technology and bioinformatics, machine learning has gained prominence in the extraction of functional peptides from extensive datasets. In this study, we comprehensively review data resources and predictors related to ACVPs published over the past two decades. In addition, we analyze the influence of various factors on model performance. We have reviewed nine ACVP-containing databases, which integrate detailed information on protein fragments effective against coronaviruses, providing crucial references for the development of antiviral drugs and vaccines. Additionally, we have assessed 15 peptide predictors for antiviral or specifically anticoronavirus activity. These predictors employ computational models to swiftly screen potential antiviral candidates, offering an efficient pathway for drug development. Our study provides conclusive results and insights into the performance of different computational methods, and sheds light on the future trajectory of bioinformatics tools for ACVPs. This work offers a representative overview of contributions to the field, with an emphasis on the crucial role of ACVPs in combating COVID-19.

A Hybrid Neuromorphic Object Tracking and Classification Framework for Real-Time Systems.

Deep learning inference that needs to largely take place on the "edge" is a highly computational and memory intensive workload, making it intractable for low-power, embedded platforms such as mobile nodes and remote security applications. To address this challenge, this article proposes a real-time, hybrid neuromorphic framework for object tracking and classification using event-based cameras that possess desirable properties such as low-power consumption (5-14 mW) and high dynamic range (120 dB). Nonetheless, unlike traditional approaches of using event-by-event processing, this work uses a mixed frame and event approach to get energy savings with high performance. Using a frame-based region proposal method based on the density of foreground events, a hardware-friendly object tracking scheme is implemented using the apparent object velocity while tackling occlusion scenarios. The frame-based object track input is converted back to spikes for TrueNorth (TN) classification via the energy-efficient deep network (EEDN) pipeline. Using originally collected datasets, we train the TN model on the hardware track outputs, instead of using ground truth object locations as commonly done, and demonstrate the ability of our system to handle practical surveillance scenarios. As an alternative tracker paradigm, we also propose a continuous-time tracker with C++ implementation where each event is processed individually, which better exploits the low latency and asynchronous nature of neuromorphic vision sensors. Subsequently, we extensively compare the proposed methodologies to state-of-the-art event-based and frame-based methods for object tracking and classification, and demonstrate the use case of our neuromorphic approach for real-time and embedded applications without sacrificing performance. Finally, we also showcase the efficacy of the proposed neuromorphic system to a standard RGB camera setup when simultaneously evaluated over several hours of traffic recordings.

Deciphering the Feature Representation of Deep Neural Networks for High-Performance AI.

AI driven by deep learning is transforming many aspects of science and technology. The enormous success of deep learning stems from its unique capability of extracting essential features from Big Data for decision-making. However, the feature extraction and hidden representations in deep neural networks (DNNs) remain inexplicable, primarily because of lack of technical tools to comprehend and interrogate the feature space data. The main hurdle here is that the feature data are often noisy in nature, complex in structure, and huge in size and dimensionality, making it intractable for existing techniques to analyze the data reliably. In this work, we develop a computational framework named contrastive feature analysis (CFA) to facilitate the exploration of the DNN feature space and improve the performance of AI. By utilizing the interaction relations among the features and incorporating a novel data-driven kernel formation strategy into the feature analysis pipeline, CFA mitigates the limitations of traditional approaches and provides an urgently needed solution for the analysis of feature space data. The technique allows feature data exploration in unsupervised, semi-supervised and supervised formats to address different needs of downstream applications. The potential of CFA and its applications for pruning of neural network architectures are demonstrated using several state-of-the-art networks and well-annotated datasets across different disciplines.

Physics-Informed Fully Convolutional Networks for Forward Prediction of Temperature Field and Inverse Estimation of Thermal Diffusivity

Abstract Physics-informed neural networks (PINNs) are a novel approach to solving partial differential equations (PDEs) through deep learning. They offer a unified manner for solving forward and inverse problems, which is beneficial for various engineering problems, including heat transfer analysis. However, traditional PINNs suffer from low accuracy and efficiency due to the fully-connected neural network framework and the method to incorporate physical laws. In this paper, a novel physics-informed learning architecture, named physics-informed fully convolutional networks (PIFCNs), is developed to simultaneously solve forward and inverse problems in thermal conduction. The use of fully convolutional networks (FCNs) significantly reduces the density of connections. Thus, the computational cost is reduced. With the advantage of the nodal-level match between inputs and outputs in FCNs, the output solution can be used directly to formulate discretized PDEs via a finite difference method, which is more accurate and efficient than the traditional approach in PINNs. The results demonstrate that PIFCNs can flexibly implement Dirichlet and Neumann boundary conditions to predict temperature distribution. Remarkably, PIFCNs can also estimate unknown thermal diffusivity with an accuracy exceeding 99%, even with incomplete boundaries and limited sampling data. The results obtained from PIFCNs outperform those obtained from PINNs.

Time-Aware Missing Healthcare Data Prediction Based on ARIMA Model.

Healthcare uses state-of-the-art technologies (such as wearable devices, blood glucose meters, electrocardiographs), which results in the generation of large amounts of data. Healthcare data is essential in patient management and plays a critical role in transforming healthcare services, medical scheme design, and scientific research. Missing data is a challenging problem in healthcare due to system failure and untimely filing, resulting in inaccurate diagnosis treatment anomalies. Therefore, there is a need to accurately predict and impute missing data as only complete data could provide a scientific and comprehensive basis for patients, doctors, and researchers. However, traditional approaches in this paradigm often neglect the effect of the time factor on forecasting results. This article proposes a time-aware missing healthcare data prediction approach based on the autoregressive integrated moving average (ARIMA) model. We combine a truncated singular value decomposition (SVD) with the ARIMA model to improve the prediction efficiency of the ARIMA model and remove data redundancy and noise. Through the improved ARIMA model, our proposed approach (named MHDP SVD_ARIMA) can capture underlying pattern of healthcare data changes with time and accurately predict missing data. The experiments conducted on the WISDM dataset show that MHDP SVD_ARIMA approach is effective and efficient in predicting missing healthcare data.

Neuro-Optimal Event-Triggered Impulsive Control for Stochastic Systems via ADP.

This article presents a novel neural-network-based optimal event-triggered impulsive control method. First, a novel general-event-based impulsive transition matrix (GITM) is constructed to represent the probability distribution evolving characteristics regarding all system states across the impulsive actions, rather than the prefixed timing sequence. On the foundation of this GITM, the event-triggered impulsive adaptive dynamic programming (ETIADP) algorithm and its high-efficiency version (HEIADP) are developed to deal with the optimization problems for stochastic systems with event-triggered impulsive controls. It is shown that the obtained controller design scheme can reduce the computational and communication burden caused by updating the controller periodically. By analyzing the admissibility, monotonicity, and optimality properties of ETIADP and HEIADP, we further establish the approximation error bound of the neural networks to address the connection between the ideal and neural-network-based realizations of the present methods. It is proven that the iterative value functions of both the ETIADP and HEIADP algorithms fall in a small neighborhood of the optimum as the iteration index increases to infinity. By adopting a novel task synchronization mechanism, the proposed HEIADP algorithm fully utilizes the computing resources of multiprocessor systems (MPSs), while significantly reducing the memory requirement compared to traditional ADP approaches. Finally, we carry out a numerical study to show that the proposed methods can fulfill the desired goals.

Psikopatoloji Araştırmalarında Yeni Bir Yöntem: Ağ Analizi

Though network analysis has a long history in both natural and social sciences it has emerged as a new method in psychology in recent years. Unlike medical disorders, mental disorders are not observable in laboratory. However, we can identify them by the way of observable symptoms. According to the network perspective, a disorder occurs when an external event triggers a psychological symptom. Activated symptom also interacts with other symptoms and forms a pattern of symptoms. Network approach criticizes traditional categorical diagnostic approach and focuses on symptom organization. Probably, treating the most effective symptom will accelerate recovery process and provide more effective treatment. Network analysis can be used in both cross-sectional and longitudinal studies. Psychological networks provide opportunities to investigate direction of the relationship among symptoms, comorbidity, external triggers of psychological symptoms, effectiveness of treatment, comparison of symptom pattern according to sample characteristics. Despite the utility of psychological networks, accuracy of them has been questioned and certain methods to prove accuracy of networks proposed as response. Technological progress in recent years enabled network analysis to be more eligible in psychology. R Statistics software is very useful in network analysis which is totally free and open sourced and supported by many additional packages. This review article aims is to provide information about usage of network analysis in psychology, especially in clinical research. In the first part historical and theoretical background of network analysis was introduced and in the following parts structure, validity of psychological networks and R Statistics Software which is used for conducting network analysis were explained briefly.

Komposisi Musik Semarak Sipai Bersumber Dari Beruji Dol Dalam Upacara Tabot Di Kota Bengkulu

Music is a means of expressing the soul to communicate and interact between living creatures. Through music we can express various ideas with various colors of sound produced by any object that can accommodate the idea itself. Sounds and voices are arranged well so that a musical work is created as a harbor for ideas that are full of messages and meaning. This work begins with research on the Bruji Dol performance and from the results of the analysis, a conclusion is drawn to take one part, namely the song Girik" which is contained in the Bruji Dol performance in the Tabot ceremony in the city of Bengkulu. Beruji dol is a depiction of the spirit of war carried out by two groups, namely Tabot Bangsal and Tabot Imam. Beruji dol has an interesting musical case, namely that there is a game concept that is "freely bound" in the tasa game of the Tamatam repertoire called "Girik". To create the musical composition "Semarak Sipai" it was worked on using the Analytical Descriptive method using a Re-interpretation of Tradition approach. Through the creation of the musical composition "Semarak Sipai", the creator tried to present several forms of novelty in various aspects of the work in accordance with the concept offered. The Tradition Re-interpretation approach is used to share musical experiences as a contribution to the development of the musical composition itself.

HT-WSO: A hybrid meta-heuristic approach-aided multi-objective constraints for energy efficient routing in WBANs

Generally, Wireless Body Area Networks (WBANs) are regarded as the collection of small sensor devices that are effectively implanted or embedded into the human body. Moreover, the nodes included in the WBAN have large resource constraints. Hence, reliable and energy-efficient data transmission plays a significant role in the implementation and in constructing of most of the merging applications. Regarded to complicated channel environment, limited power supply, as well as varying link connectivity has made the construction of WBANs routing protocol become difficult. In order to provide the routing protocol in a high energy-efficient manner, a new approach is suggested using hybrid meta-heuristic development. Initially, all the sensor nodes in WBAN are considered for experimentation. In general, the WBAN is comprised of mobile nodes as well as fixed sensor nodes. Since the existing models are ineffective to achieve high energy efficiency, the new routing protocol is developed by proposing the Hybrid Tunicate-Whale Swarm Optimization (HT-WSO) algorithm. Subsequently, the proposed work considers the multiple constraints for deriving the objective function. The network efficiency is analyzed using the objective function that is formulated by distance, hop count, energy, path loss, and load and packet loss ratio. To attain the optimum value, the HT-WSO derived from Tunicate Swarm Algorithm (TSA) and Whale Optimization Algorithm (WOA) is employed. In the end, the ability of the working model is estimated by diverse parameters and compared with existing traditional approaches. The simulation outcome of the designed method achieves 13.3%, 23.5%, 25.7%, and 27.7% improved performance than DHOA, Jaya, TSA, and WOA. Thus, the results illustrate that the recommended protocol attains better energy efficiency over WBANs.

Single Incision Broström-Gould Surgery With Peroneal Debridement and Calcaneal Osteotomy

Objective: Chronic lateral ankle instability (CLAI) with hindfoot varus is a common condition that can be treated with various surgical approaches. The Broström-Gould procedure is the gold-standard surgery for CLAI, and a sliding lateralizing calcaneal osteotomy (SLCO) is a common procedure for CLAI with hindfoot varus. The purpose of this paper is to describe a single-incision approach for the Broström-Gould repair with concomitant peroneal tendon debridement and an SLCO. Patients and Methods: The study retrospectively reviewed 189 cases of lateral ankle ligamentous repair with peroneal tendon debridement and an SLCO between 2011 and 2020. Of these, 53 patients had a single-incision approach, meeting the major inclusion criteria. The remaining patients had 2 incisions. Inclusion criteria for this study required patients to be at least 18 years of age, have a preoperative diagnosis of CLAI with hindfoot varus, and receive an isolated one-incision approach for a Broström-Gould procedure with concomitant peroneal tendon debridement and an SLCO. Results: The single-incision technique has shown to be equally effective with potential for decreased wound and neurovascular complications in comparison to a traditional multi-incisional approach. Conclusions: The single-incision technique for the Broström-Gould repair with concomitant peroneal tendon debridement and an SLCO is a safe and effective approach for treating CLAI with hindfoot varus. This technique offers adequate visualization of the lateral ankle ligaments, peroneal tendons, and lateral calcaneus while still achieving excellent postoperative cosmesis.

Timing, sequence, duration and rate of deformation in fold-and-thrust belts: a review of traditional approaches and recent advances from absolute dating (K–Ar illite/U–Pb calcite) of brittle structures

Timing, sequence, duration and rate of deformation in fold-and-thrust belts: a review of traditional approaches and recent advances from absolute dating (K–Ar illite/U–Pb calcite) of brittle structures

Dual-Frequency Lidar for Compressed Sensing 3D Imaging Based on All-Phase Fast Fourier Transform

Lidar, with its advantages of high measurement accuracy, fine angular resolution, and strong anti-interference capability, plays a pivotal role in the field of scene depth information acquisition. Traditional approaches to achieving lateral spatial resolution in imaging include raster scanning and array detectors. The former necessitates frequent scanning to acquire depth maps, resulting in time consumption and instability. The latter encounters challenges such as high dark count rates, pixel crosstalk, and excessive costs for obtaining high-resolution images using array detectors. The introduction of compressed sensing (CS) offers a novel perspective on realizing non-scanning three-dimensional imaging. In this context, we propose a novel three-dimensional imaging system that combines CS with coherent dual-frequency continuous-wave lidar and utilizes the all-phase fast Fourier transform to extract both amplitude and phase information. This system requires only M measurements, and through a reconstruction algorithm, it achieves the inversion of depth information for N-pixel scenes (M << N). Integrating cost-effective components such as digital micromirrors and single-point detectors, this affordable system accomplishes three-dimensional imaging of a single target. Notably, it significantly reduces the required number of measurements while concurrently ensuring enhanced eye safety and signal-to-noise ratio.