Identification Capability Research Articles

Navigating Modern Threats: The Imperative of Strengthening Cybersecurity

Cybersecurity has become crucial in today's changing world to protect data and promote technological advancements, according to a recent online study. In a world where networks and IoT devices are increasingly interconnected and vulnerable, to attacks cyber threats have become a concern as discussed in the research paper I came across. The study delves into issues related to cybersecurity risks. Emphasizes the importance of implementing strong security measures to prevent unauthorized access and cyber breaches. The research explores studies by areas that need more attention while stressing the value of modern approaches, like artificial intelligence (AI) and machine learning (ML) in boosting threat identification and reaction capabilities. This article discusses the importance of cybersecurity, in maintaining stability and security through analysis. It emphasizes the need for companies to adopt technologies in their security measures for managing risks. The results of this article have implications, for fostering cooperation to tackle cybersecurity issues and contribute to securing a digital future. Keywords Cyber Security, Cyber Attacks, Challenges, Threats, Vulnerabilities

An Attention-Based Spatial-Spectral Joint Network for Maize Hyperspectral Images Disease Detection

Maize is susceptible to pest disease, and the production of maize would suffer a significant decline without precise early detection. Hyperspectral imaging is well-suited for the precise detection of diseases due to its ability to capture the internal chemical characteristics of vegetation. However, the abundance of redundant information in hyperspectral data poses challenges in extracting significant features. To overcome the above problems, in this study we proposed an attention-based spatial-spectral joint network model for hyperspectral detection of pest-infected maize. The model contains 3D and 2D convolutional layers that extract features from both spatial and spectral domains to improve the identification capability of hyperspectral images. Moreover, the model is embedded with an attention mechanism that improves feature representation by focusing on important spatial and spectral-wise information and enhances the feature extraction ability of the model. Experimental results demonstrate the effectiveness of the proposed model across different field scenarios, achieving overall accuracies (OAs) of 99.24% and 97.4% on close-up hyperspectral images and middle-shot hyperspectral images, respectively. Even under the condition of a lack of training data, the proposed model performs a superior performance relative to other models and achieves OAs of 98.29% and 92.18%. These results proved the validity of the proposed model, and it is accomplished efficiently for pest-infected maize detection. The proposed model is believed to have the potential to be applied to mobile devices such as field robots in order to monitor and detect infected maize automatically.

Does a Fractional-Order Recurrent Neural Network Improve the Identification of Chaotic Dynamics?

This paper presents a quantitative study of the effects of using arbitrary-order operators in Neural Networks. It is based on a Recurrent Wavelet First-Order Neural Network (RWFONN), which can accurately identify several chaotic systems (measured by the mean square error and the coefficient of determination, also known as R-Squared, r2) under a fixed parameter scheme in the neural algorithm. Using fractional operators, we analyze whether the identification capabilities of the RWFONN are improved, and whether it can identify signals from fractional-order chaotic systems. The results presented in this paper show that using a fractional-order Neural Network does not bring significant advantages in the identification process, compared to an integer-order RWFONN. Nevertheless, the neural algorithm (modeled with an integer-order derivative) proved capable of identifying fractional-order dynamical systems, whose behavior ranges from periodic and multi-stable to chaotic oscillations. That is, the performances of the Neural Network model with an integer-order derivative and the fractional-order network are practically identical, making the use of fractional-order RWFONN-type networks meaningless. The results deepen the work previously published by the authors, and contribute to developing structures based on robust and generic neural algorithms to identify more than one chaotic oscillator without retraining the Neural Network.

Identifying influential nodes in social networks via improved Laplacian centrality

Identifying influential nodes in social networks has significant applications in terms of social analysis and information dissemination. How to capture the crucial features of influential nodes without increasing the computational complexity is an urgent issue to be solved in the context of big data. Laplacian centrality (LC) measures nodal influence by computing nodes' degree, making it extremely low complexity. However, there is still significant room for improvement. Consequently, we propose the improved Laplacian centrality (ILC) to identify influential nodes based on the concept of self-consistent. Identifying results on 9 real networks prove that ILC is superior to LC and other 6 classical measures in terms of ranking accuracy, top-k nodes identification and discrimination capability. Moreover, the computational complexity of ILC has not significantly increased compared to LC, and remains the linear order of magnitude O(m). Additionally, ILC has excellent robustness and universality such that there is no need to adjust parameters according to different network structures.

Fault diagnosis of transformer based on XGBoost

Abstract. With the continuous development of technology, the fault diagnosis of transformer is also continuously advancing. Traditional fault diagnosis methods such as the three-ratio method and the characteristic gas method have the disadvantages of missing coding and limited identification capabilities. Data-driven fault diagnosis methods can integrate various information for data analysis to accurately diagnose the type of fault. This paper proposes a fault diagnosis method for transformers based on the XGBoost model. First, the input data is selected for the ratio feature engineering, and the obtained ratios are also used as input data, and then the input data is normalized for data preprocessing. Secondly, the XGBoost model is introduced, which minimizes the prediction errors in each round of gradient boosting, thereby improving the accuracy and recall rate. Finally, through case analysis, comparisons are made with other methods in various performance indicators, and the accuracy is improved. The results show that the XGBoost is more accurate in the fault diagnosis of transformers, proving that the research has certain value and significance.

Serum metabolite and metal ions profiles for breast cancer screening

Enhancing early-stage breast cancer detection requires integrating additional screening methods with current diagnostic imaging. Omics screening, using easily collectible serum samples, could serve as an initial step. Alongside biomarker identification capabilities, omics analysis allows for a comprehensive analysis of prevalent histological types—DCIS and IDC. Employing metabolomics, metallomics, and machine learning, could yield accurate screening models with valuable insights into organism responses. Serum samples of confirmed breast cancer patients were utilized to analyze metabolite and metal ion profiles, using two distinct analysis methods, proton NMR for metabolomics and ICP-OES for metallomics. The resulting responses were then subjected to discriminant analysis, progression biomarker exploration, examination of correlations between patients’ metabolites and metal ions, and the impact of age and menopause status. Measured NMR spectra and metabolite relative integrals were used to achieve statistically significant discrimination through MVA between breast cancer and control groups. The analysis identified 24 metabolites and 4 metal ions crucial for discrimination. Furthermore, four metabolites were associated with disease progression. Additionally, there were important correlations and relationships between metabolite relative integrals, metal ion concentrations, and age/menopausal status subgroups. Quantified relative integrals allowed for discrimination between studied subgroups, validated with a holdout set. Feature importance and statistical analysis for metabolomics and metallomics extracted a set of common entities which in combination provides valuable insights into ongoing molecular disturbances and disease progression.

Comparison of Multi-Armed Bandit Algorithms in Advertising Recommendation Systems

In today's rapidly evolving online environment, advertising recommendation systems utilize multi-armed bandit algorithms like dynamic collaborative filtering Thompson sampling (DCTS), upper confidence bound based on recommender system (UCB-RS), and dynamic -greedy algorithm (DEG) to optimize ad displays and enhance click-through rates (CTR). These algorithms must adapt to limited information and update strategies based on immediate feedback.This study employs an experimental comparison to assess the performance of the DCTS, UCB-RS, and DEG algorithms using the click-through rate prediction database from Kaggle. Five experimental sets under varied parameter settings were analyzed, employing the Receiver Operating Characteristic (ROC) curve, accuracy, and area under the curve (AUC) metrics.Results show that the DEG algorithm consistently outperforms the others, achieving higher AUC values and demonstrating robust sample identification capabilities. DEG also exhibits superior precision at high recall levels, showcasing its potential in dynamic advertising environments. Its dynamic adjustment strategy effectively balances exploration and exploitation, optimizing ad displays.The findings suggest that DEG's adaptability and stability make it particularly suitable for dynamic ad recommendation scenarios. Future research should focus on optimizing DEG's parameter settings and possibly integrating UCB-RS's exploration mechanisms to enhance performance and develop more effective strategies for advertising recommendation systems.

Robust conductive hydrogel advances self-powered intelligent sports monitoring and fair judging

Currently, combining multimodal perceptual ability with mechanical robustness poses a significant challenge for developing hydrogel-based flexible electronics. Herein, a robust conductive hydrogel was successfully synthesized by introducing cellulose nanocrystals (CNC) as nanofillers and lithium chloride (LiCl) as the conductive component into the copolymer network through a one-pot crosslinking procedure in a water-dimethyl sulfoxide binary solvent. Besides excellent stretchable, self-adhesive, anti-freezing, self-healing, and anti-swelling properties, tunable and reversible optical property was realized due to the polarity-induced microphase separation of the hydrophilic and hydrophobic group in the zwitterionic sulfobetaine chains, enabling cyclic information encryption and decryption. An all-weather wearable sensor array was thus developed for multi-modal sensing of various environmental stimuli, including mechanical, and thermal variations. Specifically, the intensity and spatial distribution of strain can be detected almost in real time within a broad strain range, thus realizing speech recognation, text-to-speech and human electrocardiogram signal acquisition and underwater non-contact sensing functions. Notably, the CPAMD-based triboelectric nanogenerator demonstrates impressive temperature tolerance (−40 °C and 60 °C), and remarkable material identification capability. A non-contact proximity sensing sensor and a foul detection system were thus developed by coupling the piezoresistive, triboelectric and capacitive sensing metrics to serve as an electronic referee for identifying foul in curling. This work provides a valuable reference toward combining excellent mechanical robustness with balanced electrical performance for tactile perception and intelligent sports monitoring.

Clinical efficacy of olfactory training using aromatic traditional Chinese medicine in managing olfactory dysfunction induced by SARS-CoV-2.

The aim of this study is to assess the clinical efficacy of olfactory training using aromatic traditional Chinese medicine (TCM) for addressing severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2)-induced olfactory dysfunction, while also exploring the factors that influence the observed efficacy. 172 outpatients with SARS-CoV-2-related olfactory dysfunction were randomized into two groups. The experimental group received olfactory training with TCM aromatics (ginger, Pericarpium Citri Reticulatae, Santali Albi Lignum, Styrax), while the control group used non-TCM aromatics (phenyl ethanol-rose, menthol-mint, citronellal-lemon, eugenol-clove) for 24 weeks. Olfactory function was assessed using the Sniffin' Sticks test and TDI (threshold-discrimination-identification) scores at baseline, 1, 3, and 6 months post-treatment. Response rates at 1, 3, and 6 months post-treatment were 3.66%, 25.61%, and 43.90% in the experimental group, and 4.94%, 23.46%, and 43.21% in the control group. The TDI scores of the experimental group and the control group were statistically different before and after treatment. At 3 and 6 months post-treatment, TDI scores increased significantly, with enhanced odor discrimination and identification capabilities in both groups compared to pre-treatment, while the odor detection threshold was not improved compared with that before treatment. At the 3- and 6-month follow-ups, experimental group showed significantly higher self-rated sleep and anxiety scores than controls, indicating notable improvement in both after treatment. Olfactory training with aromatic TCM offers an effective treatment for SARS-CoV-2-induced olfactory dysfunction, improving odor discrimination, identification without significant differences compared to conventional aromatics, besides, it may also improve anxiety and sleep quality.

The unreliability of crackles: insights from a breath sound study using physicians and artificial intelligence

Background and introductionIn comparison to other physical assessment methods, the inconsistency in respiratory evaluations continues to pose a major issue and challenge.ObjectivesThis study aims to evaluate the difference in the identification ability of different breath sound.Methods/descriptionIn this prospective study, breath sounds from the Formosa Archive of Breath Sound were labeled by five physicians. Six artificial intelligence (AI) breath sound interpretation models were developed based on all labeled data and the labels from the five physicians, respectively. After labeling by AIs and physicians, labels with discrepancy were considered doubtful and relabeled by two additional physicians. The final labels were determined by a majority vote among the physicians. The capability of breath sound identification for humans and AI was evaluated using sensitivity, specificity and the area under the receiver-operating characteristic curve (AUROC).Results/outcomeA total of 11,532 breath sound files were labeled, with 579 doubtful labels identified. After relabeling and exclusion, there were 305 labels with gold standard. For wheezing, both human physicians and the AI model demonstrated good sensitivities (89.5% vs. 86.0%) and good specificities (96.4% vs. 95.2%). For crackles, both human physicians and the AI model showed good sensitivities (93.9% vs. 80.3%) but poor specificities (56.6% vs. 65.9%). Lower AUROC values were noted in crackles identification for both physicians and the AI model compared to wheezing.ConclusionEven with the assistance of artificial intelligence tools, accurately identifying crackles compared to wheezing remains challenging. Consequently, crackles are unreliable for medical decision-making, and further examination is warranted.

Emitter Signal Deinterleaving Based on Single PDW with Modulation-Hypothesis-Augmented Transformer

Radar emitter signal deinterleaving based on pulse description words (PDWs) is a challenging task in the field of electronic warfare because of the parameter sparsity and uncertainty of PDWs. In this paper, a modulation-hypothesis-augmented Transformer model is proposed to identify emitters from a single PDW with an end-to-end manner. Firstly, the pulse features are enriched by the modulation hypothesis mechanism to generate I/Q complex signals from PDWs. Secondly, a multiple-parameter embedding method is proposed to expand the signal discriminative features and to enhance the identification capability of emitters. Moreover, a novel Transformer deep learning model, named PulseFormer and composed of spectral convolution, multi-layer perceptron, and self-attention based basic blocks, is proposed for discriminative feature extraction, emitter identification, and signal deinterleaving. Experimental results on synthesized PDW dataset show that the proposed method performs better on emitter signal deinterleaving in complex environments without relying on the pulse repetition interval (PRI). Compared with other deep learning methods, the PulseFormer performs better in noisy environments.

Acoustic Streaming Tunnel Enables Particle Velocity Stretching in Multiplex Flow Cytometry.

Multiplexed flow cytometry, known for its powerful high-throughput identification capability, is widely applied across various biomedical and clinical fields. However, classical flow cytometry relies on multichannel lasers and detectors, which are significant in cost and size, limiting their application in miniaturized assays. Herein, we developed an acoustic streaming-based flow cytometry technique that focuses on multisized microbeads flowing sheathlessly. This method enables the discrimination of particle types and the quantification of target protein concentrations using only a single detector. Microbeads of different sizes exhibit distinct behaviors in the continuous acoustic streaming tunnel, leading to an increased velocity difference during their transition under the laser spot. Consequently, a size detection method based on "velocity stretching" has been established. A multiplex assay of three proteins: cardiac troponin I, creatine kinase-MB and myoglobin, in acute myocardial infarction is performed to validate the feasibility and evaluate the performance of the system. This new multiplexed flow cytometry strategy is expected to enable low-cost and onsite detection of multiple biomarkers.

Individual Buffalo Identification Through Muzzle Dermatoglyphics Images using Deep Learning Approaches

Animal identification is essential for routine farm operations, residue traceback, insurance, and ownership management. Owing to their uniqueness, incorrigible nature, tamperproof over time, environment-friendly, and pain-free, visual biometrics-based animal identification has recently gained momentum over traditional animal identification methods. Among visual biometrics-based cues, muzzle identification is a simple and relatively low-cost method. Therefore, to address the inherent significant limitations of conventional animal identification systems, we undertook this investigation to collect a database of digital images of muzzles that works as a benchmark, to apply deep learning frameworks to identify individual buffaloes from their muzzle images, and to compare their accuracy in terms of their identification capabilities. Muzzle images of 198 Surti buffaloes were subjected to transfer learning and fine-tuning processes in deep-learning neural networks. The performance was recorded for each pre-train model (ResNet50, InceptionV3, VGG16, AlexNet) with different hyperparameters of the epoch, batch size, and learning rate. A perusal of the data revealed that ResNet50 has the highest train accuracy (99.8%) and test accuracy (99.69%) among all four models used. AlexNet has the lowest train accuracy (90.8%) among the models. The findings concluded that all these four models could be applied to identify individual buffaloes; however, ResNet50 had the highest accuracy, and deep learning applications have great potential for individual buffalo identification and are promising tools for precision livestock farming

Elemental fingerprinting based on ICP-MS/OES and machine learning: Sample digestion-free and standard-free strategy for geographical origin identification of Chinese liquor

The significant price disparity of Chinese baijiu originating from various regions has unfortunately partially given rise to the proliferation of counterfeit and substandard products, and this may cause disturbance in the market economy. Ensuring the traceability and differentiation of baijiu by its origin is therefore of paramount importance. In this study, the Baijiu samples were straightforward dilution without sample digestion, followed by the detection of elemental signal intensities using ICP-MS and ICP-OES without accurate quantification, to rapidly construct their multi-elemental fingerprints. Heatmap and HCA (hierarchical cluster analysis) were used for data visualization and tentative classification, respectively. PCA (principal component analysis) was implemented to corroborate the classification outcomes. Subsequently, the supervised OPLS-DA (orthogonal partial least squares discrimination analysis) was deployed to enhance the identification and predictive capabilities for the Baijiu samples. The unequivocal discrimination of the three distinct Baijiu origins with 100% classification accuracy. The validation values R2 and Q2 approached 1.0, indicating a great fit and prediction capability of the OPLS-DA model. The VIP plot evaluated the importance of elemental variables, revealing that the key contributions were primarily from elements K, Ca, Na, Mg, and Al. Collectively, these findings demonstrate the efficacy of chemometric techniques, when integrated with multi-elemental fingerprints based on their signal intensities only, in identifying the geographical origins of the Baijiu. This approach holds promise for applications in quality control and anti-counterfeiting measures in the baijiu industry.

Particle identification capability of a homogeneous calorimeter composed of oriented crystals

Recent studies have shown that the electromagnetic shower induced by a high-energy electron, positron or photon incident along the axis of an oriented crystal develops in a space more compact than the ordinary. On the other hand, the properties of the hadronic interactions are not affected by the lattice structure. This means that, inside an oriented crystal, the natural difference between the hadronic and the electromagnetic shower profile is strongly accentuated. Thus, a calorimeter composed of oriented crystals could be intrinsically capable of identifying more accurately the nature of the incident particles, with respect to a detector composed only of non-aligned crystals. Since no oriented calorimeter has ever been developed, this possibility remains largely unexplored and can be investigated only by means of numerical simulations. In this work, we report the first quantitative evaluation of the particle identification capability of such a calorimeter, focusing on the case of neutron-gamma discrimination. We demonstrate through Geant4 simulations that the use of oriented crystals significantly improves the performance of a Random Forest classifier trained on the detector data. This work is a proof that oriented calorimeters could be a viable option for all the environments where particle identification must be performed with a very high accuracy, such as future high-intensity particle physics experiments and satellite-based γ-ray telescopes.

Stand-off hazardous materials identification based on near-infrared hyperspectral imaging combined with convolutional neural network

Near-infrared (NIR) hyperspectral imaging enables rapid, non-contact imaging of hazardous materials in a non-destructive manner, allowing for analysis based on spectral reflection information. However, using traditional methods, it is challenging to identify hazardous materials with less distinct spectral reflection features. This study utilizes a self-built NIR hyperspectral imaging system and proposes a new approach. Using a convolutional neural network (CNN), This allows for the rapid completion of high-throughput spectral screening, marking suspicious spectra at spatial points. we sophisticatedly classified six hazardous material types, generating impactful warning images. The optimized CNN demonstrated superior performance (accuracy 91.08 %, recall 91.15 %, specificity 91.62 %, precision 90.17 %, and 0.924 F1 score) compared to SVM and KNN methods. Our study included multitask validation tests, revealing a sensitive detection of 10 mg/cm2 for ammonium nitrate and trinitrotoluene, capable of identifying over 100 targets simultaneously. By simulating real-world scenarios, we successfully detected hazardous chemicals scattered on the ground and accurately identified these hazardous materials in glass and thin plastic products. Even in situations where clothing obstructed the view, we could still correctly identify hazardous chemicals and generate corresponding warning images. Our system demonstrated precise identification capabilities even amidst complex backgrounds. This method provides an accurate and rapid solution for identifying and locating hazardous chemicals, laying a strong foundation for the next steps in non-contact, long-distance quantitative determination of chemical concentrations. This study highlights the effective application of CNN in non-contact, long-distance classification, and recognition of hazardous materials, paving the way for further scientific and engineering applications.

Analysis of motion performance and motion mode recognition in filament propulsion

Abstract In this study, the full free motion of the filament in vortex structures is numerically simulated, and the influence of model parameters and horizontal position on the propulsion efficiency of the filament is investigated. With the “data + physics” approach with the immersed boundary method and Random Forest (RF) method, an RF model is initially employed to screen 189 sets of numerical experiments using a minimal amount of flow field physical data to select experimental groups capable of generating vortex bands. The selected experimental groups are then used to complete numerical experiments, followed by the re-introduction of a minimal amount of data and the use of the RF model to identify the motion modes of the filament. The experimental results indicate that the stretching coefficient of the filament has a negative effect on the thrust coefficient when the filament is in the vortex. The thrust of the filament decreases with increasing horizontal distance. The RF model demonstrates excellent identification capabilities, correctly classifying the vortex street structure with only three sets of data from three test points, achieving a high accuracy rate of 95% in identifying the motion modes of the filament.

Nanoparticles as a novel key driver for the isolation and detection of circulating tumour cells

Circulating tumour cells (CTCs), derived from primary tumours, play a pivotal role in cancer metastasis by migrating into the peripheral bloodstream. These cells are paramount in clinical research, serving as early diagnostic markers for metastatic cancer. Analysing CTC counts and their biomarker characteristics can provide invaluable insights into tumour identification, profiling, and metastatic capabilities. However, the rarity and diverse nature of CTCs in the bloodstream present significant challenges to their isolation and detection, especially in the initial stages of metastasis. Recent advancements in nanotechnology have led to the development of innovative CTC separation and detection methods. This review focuses on applying nanoparticles, nanomaterials, and microfluidic platforms to simplify the isolation and detection of CTCs. The infusion of nanotechnology in this field marks a crucial turning point, enabling the necessary progress to advance CTC research.

High-performance virtual sensors array based on a single-chip FBAR for volatile organic compounds (VOCs) detection and classification

The detection and classification of volatile organic compounds (VOCs) are of great significance in atmospheric environment monitoring and have garnered extensive attention in recent decades. This study proposes a virtual sensor array (VSA) for VOC detection and classification fabricating a single-chip high-Q-factor film bulk acoustic resonator (FBAR) functionalized with MIL-101(Cr) thin film, known for its porous crystal structure and high specific surface area. The sensing performance of five different VOCs was evaluated to characterize the VOCs, achieving high detection accuracy calibrated with the Langmuir adsorption model. The FBAR’s frequency response was utilized to determine the sensitivity and limit of detection (LOD), yielding sensitivity and LOD of 85.45 Hz/ppm and 51 ppm, respectively, for isopropanol. The identification and classification capabilities of the proposed VSA for VOCs and their mixtures were assessed using statistical methods and machine learning algorithms, realizing classification accuracies of 100 % for pure VOCs and 94.6 % for mixtures. The developed VSA demonstrates superior performances in VOC detection and classification, with potential applications across various domains.

Constraining non-unitary neutrino mixing using matter effects in atmospheric neutrinos at INO-ICAL

The mass-induced neutrino oscillation is a well established phenomenon that is based on the unitary mixing among three light active neutrinos. Remarkable precision on neutrino mixing parameters over the last decade or so has opened up the prospects for testing the possible non-unitarity of the standard 3ν mixing matrix, which may arise in the seesaw extensions of the Standard Model due to the admixture of three light active neutrinos with heavy isosinglet neutrinos. Because of this non-unitary neutrino mixing (NUNM), the oscillation probabilities among the three active neutrinos would be altered as compared to the probabilities obtained assuming a unitary 3ν mixing matrix. In such a NUNM scenario, neutrinos can experience an additional matter effect due to the neutral current interactions with the ambient neutrons. Atmospheric neutrinos having access to a wide range of energies and baselines can experience a significant modifications in Earth’s matter effect due to NUNM. In this paper, we study in detail how the NUNM parameter α32 affects the muon neutrino and antineutrino survival probabilities in a different way. Then, we place a comparable and complementary constraint on α32 in a model independent fashion using the proposed 50 kt magnetized Iron Calorimeter (ICAL) detector under the India-based Neutrino Observatory (INO) project, which can efficiently detect the atmospheric νμ and ν¯μ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$ {\\overline{\ u}}_{\\mu } $$\\end{document} separately in the multi-GeV energy range. Further, we discuss the advantage of charge identification capability of ICAL and the impact of uncertainties in oscillation parameters while constraining α32. We also compare the α32 sensitivity of ICAL with that of future long-baseline experiments DUNE and T2HK in isolation and combination.