Identification Method Research Articles

Characterization and Differentiation of Candida auris on Dixon’s Agar Using Raman Spectroscopy

Candida auris, an emerging multidrug-resistant fungal pathogen, poses significant challenges in healthcare settings due to its high misidentification rate and resilience to treatments. Despite advancements in diagnostic tools, a gap remains in rapid, cost-effective identification methods that can differentiate C. auris from other Candida species, particularly on non-standard culture media. We used Raman spectroscopy to characterize C. auris grown on modified Dixon’s agar (mDixon) and differentiated it from Candida albicans and Candida parapsilosis. Key Raman spectral markers at 1171 cm−1 and 1452 cm−1, linked to mannan and β-glucan composition, differentiated C. auris into two subgroups, A and B. Despite the spectral similarities of groups A and B with C. albicans and C. parapsilosis, respectively, all Candida species were distinguishable through principal component analysis (PCA). Additionally, this study is the first to demonstrate the distinct spectral signature of mDixon agar, achieved through spatially offset Raman spectroscopy (SORS), which enables accurate discrimination between the culture medium and fungal samples. The observed inter-individual variability within C. auris, coupled with the spectral overlap between C. auris subgroups and other Candida species, highlights a major challenge in differentiating closely related fungi due to their similar molecular composition. Enhancements in spectral resolution and further fluorescence minimization from the culture medium are needed to reliably detect the subtle biochemical differences within these species. Despite these challenges, the results underscore the potential of Raman spectroscopy as a real-time, non-destructive, and complementary tool for fungal pathogen identification.

Evade Unknown Pursuer via Pursuit Strategy Identification and Model Reference Policy Adaptation (MRPA) Algorithm

The game of pursuit–evasion has always been a popular research subject in the field of Unmanned Aerial Vehicles (UAVs). Current evasion decision making based on reinforcement learning is generally trained only for specific pursuers, and it has limited performance for evading unknown pursuers and exhibits poor generalizability. To enhance the ability of an evasion policy learned by reinforcement learning (RL) to evade unknown pursuers, this paper proposes a pursuit UAV attitude estimation and pursuit strategy identification method and a Model Reference Policy Adaptation (MRPA) algorithm. Firstly, this paper constructs a Markov decision model for the pursuit–evasion game of UAVs that includes the pursuer’s attitude and trains an evasion policy for a specific pursuit strategy using the Soft Actor–Critic (SAC) algorithm. Secondly, this paper establishes a novel relative motion model of UAVs in pursuit–evasion games under the assumption that proportional guidance is used as the pursuit strategy, based on which the pursuit UAV attitude estimation and pursuit strategy identification algorithm is proposed to provide adequate information for decision making and policy adaptation. Furthermore, a Model Reference Policy Adaptation (MRPA) algorithm is presented to improve the generalizability of the evasion policy trained by RL in certain environments. Finally, various numerical simulations imply the precision of pursuit UAV attitude estimation and the accuracy of pursuit strategy identification. Also, the ablation experiment verifies that the MRPA algorithm can effectively enhance the performance of the evasion policy to deal with unknown pursuers.

Accelerated strategies for impact identification of composite laminate using sparse sensor networks and model-based virtual time reversal

Barely visible impact damages are notorious threats to the health of composite structures. Detection of the impact is critical to promise the performance of composite structures. Recently, the authors proposed an impact identification method based on sparse sensor networks and virtual time reversal, which is verified on composite laminates. However, the computational demands of virtual time reversal simulations pose a notable limitation to this approach. To improve the efficiency of the method, several acceleration measures are developed in this article. First, the re-emitted wavefield analysis is accelerated using a frequency-domain method rather than the time-domain simulation, which largely reduces the computational costs. Subsequently, the GPU parallel acceleration algorithm is configured to further improve the efficiency. The proposed acceleration strategy is experimentally validated by various impact events on a composite laminate. The results render that the proposed acceleration strategy can promise the accuracy of the results of impact identification while significantly promoting computational efficiency, in which the time consumption plummets to only 1.94 s. Moreover, factors affecting the efficiency of the proposed method are discussed. The time consumption is positively correlated with the number of sensors involved in the identification. In addition, as the number of candidate points increases, the GPU encounters difficulty in handling all complex matrix operations simultaneously, leading to a substantial rise in computation time. Furthermore, the effect of signal lengths on the efficiency is minimal and largely negligible.

Research on damage identification of simply supported bridge based on effect size method for vehicle-bridge coupled vibration

Abstract This paper presents a novel bridge damage identification method employing Cohen's d as an effect size indicator, predicated on the detection of bridge damage through the coupled vibration between a vehicle and the bridge. By analyzing the dynamic response of the bridge as a vehicle passes over, this method effectively extracts the modal parameters of the bridge and facilitates the identification of bridge damage. Numerical models of the bridge under various damage conditions, including no damage, mid-span damage, and damage at the 1/4 and 3/4 span locations, have been constructed to substantiate the efficacy and precision of the effect size as a damage indicator. Furthermore, to address the challenge of accurately identifying damage at boundaries, which is often confounded by boundary effects, a boundary subdivision method has been defined for the detection of boundary damage. Through the implementation of a real-bridge test based on indirect measurement technology, the self-vibration frequency of the bridge was successfully extracted. This empirical data was then compared and analyzed against results from ANSYS numerical simulations, thereby validating the practicality and accuracy of the proposed method. In the final analysis, the influence of random traffic flow on the bridge damage identification results was examined. The findings indicate that the vibrations in simply supported beam bridges are intensified due to the impact of random traffic flow, which aids in enhancing the accuracy of damage identification. The introduction of this method provides a new quantitative tool for bridge health monitoring, enabling the rapid and accurate identification of bridge damage without interrupting traffic. It holds significant value for engineering applications in bridge maintenance and safety assessment.

Contact Stiffness Identification Method for Joint Surface and the Effect of Contact Stiffness on the Dynamic Response of the Cable Bracket

The cable bracket is one of the crucial components of high-speed Electric Multiple Units (EMUs), which is fixed to the axle box by bolts to support the cable. As an important parameter of the joint surface, contact stiffness significantly affects the dynamic response and service life of the cable bracket. However, it is difficult to directly measure the contact stiffness of the joint surface. As a result, an identification method is proposed in this investigation to identify the contact stiffness. A finite element model incorporating both normal and tangential stiffness parameters is developed. According to the finite element model and modal testing results, an improved particle swarm optimization algorithm is employed to identify the contact stiffness of the joint surface. The effect of the contact stiffness on the vibrations of the cable bracket is investigated. The findings indicate that pre-tightening torque significantly influences the contact stiffness of the cable bracket. The contact stiffness has a great effect on the vibrations of the cable bracket. Optimal contact stiffness notably reduces vibrations of the cable bracket, thereby extending its service life.

Multiple skin abscesses due to Nocardia neocaledoniensis: a case report and literature review

N. neocaledoniensis is a very rare infectious pathogen that causes human disease, particularly in immunocompromised individuals. In this case report, we describe the successful diagnosis of N. neocaledoniensis in a patient confirmed by mNGS and the treatment of multiple skin abscesses due to N. neocaledoniensis infection. mNGS is an important diagnostic method complementary to routine bacterial culture and identification methods, especially for rare, novel, co-infected pathogens, and pathogens that are difficult to culture. This report may provide a reference for the clinical treatment and diagnosis of N. neocaledoniensis infection in humans.

Enhancing mosquito classification through self-supervised learning

Traditional mosquito identification methods, relied on microscopic observation and morphological characteristics, often require significant expertise and experience, which can limit their effectiveness. This study introduces a self-supervised learning-based image classification model using the Bootstrap Your Own Latent (BYOL) algorithm, designed to enhance mosquito species identification efficiently. The BYOL algorithm offers a key advantage by eliminating the need for labeled data during pretraining, as it autonomously learns important features. During fine-tuning, the model requires only a small fraction of labeled data to achieve accurate results. Our approach demonstrates impressive performance, achieving over 96.77% accuracy in mosquito image analysis, with minimized both false positives and false negatives. Additionally, the model’s overall accuracy, measured by the area under the ROC curve, surpasses 99.55%, highlighting its robustness and reliability. A notable finding is that fine-tuning with just 10% of labeled data produces results comparable to using the full dataset. This is particularly valuable for resource-limited settings with limited access to advanced equipment and expertise. Our model provides a practical solution for mosquito identification, overcoming the challenges of traditional microscopic methods, such as the time-consuming process and reliance on specialized knowledge in healthcare services. Overall, this model supports personnel in resource-constrained environments by facilitating mosquito vector density analysis and paving the way for future mosquito species identification methodologies.

Experimental identification of ion cyclotron emission on HL-2A using YOLO neural network algorithm

Abstract Identification of magnetohydrodynamics (MHD) instabilities with neural networks has been extensively applied in the research of magnetically controlled fusion plasmas. Ion Cyclotron Emission (ICE) is a potential fast ion diagnostic method in burning plasmas. To assess ICE as a fast ion diagnostic for International Thermonuclear Experimental Reactor, real-time identification of ICE is required in the fast ion diagnostic flow. In the present work, we employed YOLO (You Only Look Once) to identify core and edge ICE in a large labeled database of HL-2A discharges, achieving a precision of 85.4% and a recall rate of 77.3%. Subsequent improvements to the YOLO model resulted in a noteworthy 8.3% increment in the recall rate. The developed identification method demonstrates significant potential for real-time application in identifying MHD instabilities.

Ignoring the food route underestimated human health risk from potentially toxic elements in agricultural environments of Ziyang, Shaanxi, China.

Staple food is a crucial exposure route for the human intake of potentially toxic elements (PTEs), but it has been neglected in previous human health risk (HHR) studies. Lack of attention to this issue will lead to an underestimation of HHR caused by PTEs. This study establishes a comprehensive regional identification method for health risk assessment (HRA), namely, soil-maize health risk assessment (SMHRA) and applies it to Ziyang, Shaanxi, which is a typical agricultural county. SMHRA considered the exposure pathway of staple food and utilized Monte Carlo simulation to enhance the accuracy of HRA for PTEs. Results indicated the PTE spatial heterogeneity in a soil-maize system. Introducing staple food exposure pathway would increase HHR values and probabilities 1.57-2.80 and 1.53-5.63 times than that when food route was not considered. Overall, the HHR caused by a single PTE was low, which relatively safe. The introduction of food pathway contributed to accurate estimate the HHR of As and Ni, and the risk probabilities ranged from 0.04% to 12.46%. Few areas had high levels of Ni, which pose health risks: approximately 1.8% for children and higher than 0.5% for adults. Both As and Ni had the highest contribution to HHR among all PTEs, with 33.84%-41.56% TNCR caused by As, and 54.73%-56.90% TCR created by Ni, respectively. For human health risk routes, the staple food exhibited the highest contribution to HHR among all exposure routes, with TNCR of 36.15%-56.73% and the TCR of 44.96%-64.28%. Our research imply that dietary intake of PETs must be considered in the human health risk assessment in agricultural environment, which offers the foundation for subsequent environmental risk prevention and control.

Efficacy of modal curvature damage detection in various pre-damage data assumptions and modal identification techniques

The efficacy of modal curvature approach for damage localization is discussed in the paper in the context of input data. Three modal identification methods, i.e., Eigensystem Realization Algorithm (ERA), Natural Excitation Technique with ERA (NExT-ERA) and Covariance Driven Stochastic Subspace Identification (SSI-Cov), and four methods of determining baseline data, i.e., real measurement of the undamaged state, analytical function, Finite Element (FE) model and approximation of current experimental mode shape, are considered. Practical conclusions are formulated based on analysis of two cases. The first is a laboratory beam with a notch and the second is a stonemasonry historic lighthouse with modern restoration in its upper part. The analysis shows that NExT-ERA and SSI-Cov in combination with approximation of current mode shape provide high efficacy in damage localization alongside relatively straightforward determination of baseline data. It proves that the construction of advanced FE models of a structure can be replaced with a much simpler method of baseline data acquisition. Furthermore, the research shows the structural mode shapes identified with ERA may not always indicate the presence of damage.

Fake News Detection Revisited: An Extensive Review of Theoretical Frameworks, Dataset Assessments, Model Constraints, and Forward-Looking Research Agendas

The emergence and acceptance of digital technology have caused information pollution and an infodemic on Online Social Networks (OSNs), blogs, and online websites. The malicious broadcast of illegal, objectionable and misleading content causes behavioural changes and social unrest, impacts economic growth and national security, and threatens users’ safety. The proliferation of AI-generated misleading content has further intensified the current situation. In the previous literature, state-of-the-art (SOTA) methods have been implemented for Fake News Detection (FND). However, the existing research lacks multidisciplinary considerations for FND based on theories on FN and OSN users. Theories’ analysis provides insights into effective and automated detection mechanisms for FN, and the intentions and causes behind wide-scale FN propagation. This review evaluates the available datasets, FND techniques, and approaches and their limitations. The novel contribution of this review is the analysis of the FND in linguistics, healthcare, communication, and other related fields. It also summarises the explicable methods for FN dissemination, identification and mitigation. The research identifies that the prediction performance of pre-trained transformer models provides fresh impetus for multilingual (even for resource-constrained languages), multidomain, and multimodal FND. Their limits and prediction capabilities must be harnessed further to combat FN. It is possible by large-sized, multidomain, multimodal, cross-lingual, multilingual, labelled and unlabelled dataset curation and implementation. SOTA Large Language Models (LLMs) are the innovation, and their strengths should be focused on and researched to combat FN, deepfakes, and AI-generated content on OSNs and online sources. The study highlights the significance of human cognitive abilities and the potential of AI in the domain of FND. Finally, we suggest promising future research directions for FND and mitigation.

Phenotypic and Molecular Identification of M.Canis

The current study was conducted to compare morphological and molecular identification methods for the fungus M.canis isolated from 42 patients with dermatophytosis who were clinically diagnosed by a dermatologist, after review by a consultant dermatologist and venereologist in Al-Muthanna Governorate at Al-Hussein Teaching Hospital. In the period from January to November 2024, using traditional methods of isolation and laboratory identification, and molecular methods using polymerase chain reaction (PCR) technology.

Rapid bacterial identification through volatile organic compound analysis and deep learning

BackgroundThe increasing antimicrobial resistance caused by the improper use of antibiotics poses a significant challenge to humanity. Rapid and accurate identification of microbial species in clinical settings is crucial for precise medication and reducing the development of antimicrobial resistance. This study aimed to explore a method for automatic identification of bacteria using Volatile Organic Compounds (VOCs) analysis and deep learning algorithms.ResultsAlexNet, where augmentation is applied, produces the best results. The average accuracy rate for single bacterial culture classification reached 99.24% using cross-validation, and the accuracy rates for identifying the three bacteria in randomly mixed cultures were SA:98.6%, EC:98.58% and PA:98.99%, respectively.ConclusionThis work provides a new approach to quickly identify bacterial microorganisms. Using this method can automatically identify bacteria in GC-IMS detection results, helping clinical doctors quickly detect bacterial species, accurately prescribe medication, thereby controlling epidemics, and minimizing the negative impact of bacterial resistance on society.

Bacterial profile-based body fluid identification using a machine learning approach.

Identifying the origins of biological traces is critical for the reconstruction of crime scenes in forensic investigations. Traditional methods for body fluid identification rely on chemical, enzymatic, immunological, and spectroscopic techniques, which can be sample-consuming and depend on simple color-change reactions. However, these methods have limitations when residual samples are insufficient after DNA extraction. This study aimed to develop a method for body fluid identification by leveraging bacterial DNA profiling to overcome the limitations of the conventional approaches. Bacterial profiles were determined by sequencing the hypervariable region of the 16S rRNA gene,using DNA metabarcoding of evidence collected from criminal cases. Amplicon sequence variants (ASVs) were analyzed to identify significant microbial patterns in different body fluid samples. The bacterial profile-based method demonstrated high discriminatory power with a machine learning model trained using the naïve Bayes algorithm, achieving an accuracy of over 98% in classifying samples into one of four body fluid types: blood, saliva, vaginal secretion, and mixture traces of vaginal secretions and semen. Bacterial profiling enhances the accuracy and robustness of body fluid identification in forensic analysis, providing a valuable alternative to traditional methods by utilizing DNA and microbial community data despite the uncontrollable conditions. This approach offers significant improvements in the classification accuracy and practical applicability in forensic investigations.

An Application Programming Interface (API) Sensitive Data Identification Method Based on the Federated Large Language Model

The traditional methods for identifying sensitive data in APIs mainly encompass rule-based and machine learning-based approaches. However, these methods suffer from inadequacies in terms of security and robustness, exhibit high false positive rates, and struggle to cope with evolving threat landscapes. This paper proposes a method for detecting sensitive data in APIs based on the Federated Large Language Model (FedAPILLM). This method applies the large language model Qwen2.5 and the LoRA instruction tuning technique within the framework of federated learning (FL) to the field of data security. Under the premise of protecting data privacy, a domain-specific corpus and knowledge base are constructed for pre-training and fine-tuning, resulting in a large language model specifically designed for identifying sensitive data in APIs. This paper conducts comparative experiments involving Llama3 8B, Llama3.1 8B, and Qwen2.5 14B. The results demonstrate that Qwen2.5 14B can achieve similar or better performance levels compared to the Llama3.1 8B model with fewer training iterations.

Measurable morphological features of single circulating tumor cells in selected solid tumors-A pilot study.

Liquid biopsies developed into a range of sensitive technologies aiming to analyze for example, circulating tumor cells (CTCs) in peripheral blood, which significantly deepens understanding of the metastatic process. Nevertheless, examination of CTCs is mostly limited to their enumeration and usually only 2-3 markers-based phenotyping, not offering yet sufficient insight into their biology. In contrast, quantitative analysis of their morphological details might extend our knowledge about dissemination and even improve CTC isolation or label-free identification methods dependent on their physical features such as size, and deformability. Current study was conducted to describe CTCs' and their size, shape, presence of protrusions, and micronuclei across various types of cancers (lung, n = 29; ovarian, n = 24, breast, n = 54; and prostate, n = 33). Epithelial (pan-keratins), mesenchymal (vimentin), and two exclusion markers were used to identify CTCs and classify them into four epithelial and epithelial-mesenchymal transition-related phenotypes using standardized and throughput method, imaging flow cytometry. The morphological characteristics of CTCs, including their nuclei, such as circularity, the maximum, and minimum diagonal values were determined using an open-source software QuPath. On average, detected CTCs (n = 1156) were larger, and more irregular in shape compared to leukocytes/endothelial cells (n = 400). Epithelial and mesenchymal CTCs had the largest (median = 18.2 μm) and the smallest diameter (median = 10.4 μm), respectively. In terms of cancer-specific variations, the largest CTCs were identified in lung cancer, whereas the smallest-in prostate and breast cancers. Epithelial CTCs and those negative for both epithelial and mesenchymal markers exhibited the highest degree of elongation, whereas mesenchymal CTCs were the most irregular in shape. Protrusions and micronuclei were observed extremely rarely within CTCs of breast and prostate cancer (0.6%-0.8% of CTCs). Micronuclei were observed only in epithelial and epithelial-mesenchymal CTCs. This study underscores the significant variability in the morphological features of CTCs in relation to their phenotypic classification or even the particular organ of origin, potentially influencing for example, size-dependent CTC isolation methods. It demonstrates for the first time the morphological measurements of CTCs undergoing epithelial-mesenchymal transition, and some specific morphological details (i.e., protrusions, micronuclei) within CTCs in general.

YOLOX-S-TKECB: A Holstein Cow Identification Detection Algorithm

Accurate identification of individual cow identity is a prerequisite for the construction of digital farms and serves as the basis for optimized feeding, disease prevention and control, breed improvement, and product quality traceability. Currently, cow identification faces challenges such as poor recognition accuracy, large data volumes, weak model generalization ability, and low recognition speed. Therefore, this paper proposes a cow identification method based on YOLOX-S-TKECB. (1) Based on the characteristics of Holstein cows and their breeding practices, we constructed a real-time acquisition and preprocessing platform for two-dimensional Holstein cow images and built a cow identification model based on YOLOX-S-TKECB. (2) Transfer learning was introduced to improve the convergence speed and generalization ability of the cow identification model. (3) The CBAM attention mechanism module was added to enhance the model’s ability to extract features from cow torso patterns. (4) The alignment between the apriori frame and the target size was improved by optimizing the clustering algorithm and the multi-scale feature fusion method, thereby enhancing the performance of object detection at different scales. The experimental results demonstrate that, compared to the traditional YOLOX-S model, the improved model exhibits a 15.31% increase in mean average precision (mAP) and a 32-frame boost in frames per second (FPS). This validates the feasibility and effectiveness of the proposed YOLOX-S-TKECB-based cow identification algorithm, providing valuable technical support for the application of dairy cow identification in farms.

How Severe Was the 2022 Flash Drought in the Yangtze River Basin?

Flash droughts, characterized by their rapid onset and severe impacts, have critical implications for the ecological environment and water resource security. However, inconsistent definitions of flash droughts have hindered scientific assessments of drought severity, limiting efforts in disaster prevention and mitigation. In this study, we propose a new method for explicitly characterizing flash drought events, with particular emphasis on the process of soil moisture recovery. The temporal and spatial evolution of flash droughts over the Yangtze River Basin was analyzed, and the severity of the extreme flash drought in 2022 was assessed by comparing its characteristics and impacts with those of three typical dry years. Additionally, the driving factors of the 2022 flash drought were evaluated from multiple perspectives. Results indicate that the new identification method for flash droughts is reasonable and reliable. In recent years, the frequency and duration of flash droughts have significantly increased, with the Dongting Lake and Poyang Lake basins being particularly affected. Spring and summer were identified as peak seasons for flash droughts, with the middle reaches most affected in spring, while summer droughts tend to impact the entire basin. Compared to 2006, 2011, and 2013, the flash drought in 2022 affected the largest area, with the highest number of grids experiencing two flash drought events and a development rate exceeding 15%. Moreover, the summer heat in 2022 was more extreme than in the other three years, extending from spring to fall, especially during July–August. Its evolution was driven by the Western Pacific Subtropical High, which suppressed precipitation and elevated temperatures. The divergence of water vapor flux intensified water shortages, while anomalies in latent and sensible heat fluxes increased surface evaporation and heat transfer, further disturbing the regional water cycle. This study provides valuable insights for flash drought monitoring and early warning in the context of a changing climate.

Dynamic Testing and Finite Element Model Adjustment of the Ancient Wooden Structure Under Traffic Excitation

In situ dynamic testing is conducted to study the dynamic characteristics of the wooden structure of the North House main hall. The velocity response signals on the measurement points are obtained and analyzed using the self-interaction spectral method and stochastic subspace method, yielding natural frequencies, mode shapes, and damping ratios. This study reveals that the natural frequencies and damping ratios are highly consistent between the two methods. Therefore, to eliminate errors, the average of the results from both modal identification methods is taken as the final measured modal parameters of the structure. The natural frequencies of the first and second order in the X direction were 2.097 Hz and 3.845 Hz and in the Y direction were 3.955 Hz and 5.701 Hz. The modal frequency in the Y direction of the structure exceeds that in the X direction. Concurrently, a three-dimensional finite element model was established using ANSYS 2021R1, considering the semi-rigid properties of mortise–tenon connections, and validated based on in situ dynamic testing. The sensitivity analysis indicates adjustments to parameters such as beam–column elastic modulus, tenon–mortise joint stiffness, and roof mass for finite element model refinement. Modal parameter calculations from the corrected finite element model closely approximate the measured modal results, with maximum errors of 9.41% for the first two frequencies, both within 10% of the measured resonant frequencies. The adjusted finite element model closely matches the experimental results, serving as a benchmark model for the wooden structure of North House main hall. The validation confirms the rationality of the benchmark finite element model, providing valuable insights into ancient timber structures along transportation routes.

MXene/WO3 Sensor Array with Improved SNN Algorithm for Accurate Identification of Toxic Gases.

Gas sensing is pivotal in critical areas such as industrial production and food safety. This study explores the gas classification capabilities of MXene-based gas sensors. Pure V2CTx MXene and an MXene/WO3 nanocomposite were synthesized, and MXene-based gas sensors were integrated into a 2 × 2 rudimentary electronic nose array. The tests on gas sensitivity revealed that the inclusion of WO3 nanoparticles (NPs) boosted the sensor's response to 10 ppm of NO2 from 2.82 to 3.45 at room temperature. Moreover, the sensor showcased a rapid response/recovery duration of 74.5/149.0 s, excellent environmental stability, and long-term reliable sensing performance. Furthermore, we have improved the method of accurately identifying four toxic gases detected by an MXene-based sensor array using a spiking neural network (SNN) based on the memristive system. Also, the performance of this identification method revealed that the method achieved 95.83% accuracy in the identification of the four gases. Notably, the improved SNN demonstrated approximately 5% higher accuracy than the other gas recognition algorithm. These results highlight the potential of SNN as a powerful tool to accurately and reliably identify toxic gases based on the gas sensor array.