Security Inspection Research Articles

Research on X-ray security contraband identification technology based on lightweight YOLOv8

Aiming at the difficulty of identification and localization in the detection of contraband targets in X-ray images, as well as the demand for upgrading and deployment of security equipment during the peak flow stage, a lightweight X-ray security contraband detection algorithm is proposed and optimized for YOLOv8. Firstly, the number of channels in the original network is reduced according to a predetermined ratio, which effectively reduces model parameters and computational complexity and achieves better results than the current mainstream lightweight methods, such as model pruning and detection backbone optimization. Secondly, due to the presence of objects of various scales and sizes in X-ray security inspection images, we introduced the BiFPN feature extraction strategy in the neck. We designed a simplified version of BiFPN based on the YOLOv8 network structure to effectively integrate feature information from different levels and scales. Finally, we incorporated the idea of Focal Loss to address the issue of imbalance between positive and negative samples and proposed a new regression loss function, Focal-GIoU Loss, which combines GIoU Loss. This increases the loss weight for important but difficult-to-detect samples, such as small targets and overlapping targets, making the model more focused on these critical samples. Results show that our proposed YOLO-CBF improves mAP by 1.5%, 0.9%, and 0.5% on three datasets with different levels of occlusion in PIDray, while reducing the parameter count and computational cost from the original 3.0M and 8.1G to 2.1M and 6.3G.Moreover, it performs well in comparative experiments between different models and in generalization experiments across different datasets.

Enhancing the Sensitivity and Spatial Imaging Resolution of a Hybrid X-Ray Imaging Screen via Energy Transfer at the ZnS (Ag) and a Thermally Activated Delayed Fluorescence Interface.

Novel scintillation materials have played an indispensable role in the recent remarkable progress witnessed for X-ray imaging technology. Herein, a high-performance X-ray scintillation screen was developed based on a highly efficient hybrid system combining inorganic ZnS (Ag) with thermally activated delayed fluorescence (TADF) scintillator materials via an interfacial energy transfer (EnT) mechanism. ZnS (Ag) has a high X-ray absorption capacity and functions as the initial layer for efficiently converting high-energy X-ray photons into low-energy visible light (acting as a sensitizer) while also serving as an energy donor. The TADF component, on the contrary, is an energy acceptor and forms an active scintillating layer. By harnessing TADF chromophores that can efficiently capture both singlet and triplet excitons, our composite material offers a remarkable spatial imaging resolution of 24 line pairs per millimeter, surpassing those of the majority of existing organic and inorganic scintillators. Further, our interfacial energy transfer strategy effectively amplifies the radioluminescence intensity of the TADF scintillator by a factor of 75, offering an outstanding light yield of 38,000 photons/MeV. This advancement represents a remarkable breakthrough in organic X-ray scintillation technology and is a notable achievement within the X-ray imaging field, paving the way for novel applications in medical imaging and security inspection.

Contraband classification method for X-ray security images considering sample imbalance

X-ray security image contraband classification is widely used to assist in maintaining aviation and transportation security. This paper suggests an end-to-end X-ray security inspection image classification method that takes sample imbalance into account in order to address the issues of different scales of contraband in X-ray images, challenging samples, and unbalanced positive and negative samples inherent in passenger baggage security inspection. The feature fusion module is used to enhance the model's ability to express picture edge and texture features while the multi-scale feature extraction network is used to capture the features of numerous sorts of illegal goods with various scales. Based on the cost-sensitive idea, the loss function is designed to solve the problem of dataset imbalance, and improve the classification accuracy of difficult samples. experimental results of the subset constructed on the public dataset SIXray show that the proposed method improves the mean AP index by compared with the current optimal end-to-end classification model, especially for hard-to-classify samples such as scissors, the AP index has a significant improvement effect.

Contraband detection algorithm for X‐ray security inspection images based on global semantic enhancement

AbstractAiming at the problem of low detection accuracy caused by overlapping occlusion and noise disturbance, a contraband detection algorithm for X‐ray security inspection images based on global semantic enhancement is proposed to achieve accurate contraband target detection by enhancing global semantic information. First, the disturbance suppression module is used to weaken the noise disturbance at different positions by local suppression and aggregate finer detail information. Then, the parallel cascade search module is used to capture long‐range dependencies and strengthen the representation of global semantic information, which helps the model identify contraband under overlapping occlusion. Finally, the contribution of different features is adaptively adjusted through the feature‐weighted fusion module, which promotes the effective fusion of multi‐scale features and improves the accuracy of model detection. The method in this article has been extensively evaluated and experimented on three mainstream benchmark datasets: SIXray, OPIXray, and PIDray. The mAPs of three datasets reach 93.5%, 91.9%, and 85.9%, respectively. The experimental results fully demonstrate that the method in this article has better performance compared with the latest method, which can meet the practical application requirements of real‐time target detection.

Self-supervised learning-based re-parameterization instance segmentation for hazardous object in terahertz image

Recently, terahertz imaging technology has shown widespread potential applications in the public security screening field. Previous terahertz hazardous object detection techniques primarily relied on manual recognition and image processing methods. Some solutions incorporating deep learning technologies depend on large quantities of high-quality data, making it challenging to achieve low-cost and high-performance detection. To solve the issue of dependency on large amounts of high-quality data, we propose a diversified dynamic structure You Only Look Once (DDS-YOLO) model based on masked image modeling and structural reparameterization for the instance segmentation of hazardous objects in terahertz security inspection images. To address the scarcity and annotating difficulty problems of terahertz security inspection image samples, we combine automatic data strategies with masked image modeling for self-supervised learning. We propose a multilevel feature refinement fusion mechanism to enhance the quality of learned feature representations. The backbone parameter transfer and fine-tuning training strategies are employed to achieve hazardous object instance segmentation on the terahertz dataset. To address the low detection accuracy issue caused by the poor terahertz image quality, we develop a reparameterizable hierarchical structure for the backbone, improve a multi-scale feature-integrating neck, and design a dynamically decoupled head with lower computational requirements to enhance the performance of the instance segmentation model. Experimental results demonstrate that the proposed model accurately outputs detection boxes, categories, and segmentation masks for hazardous objects with minimal training samples. The comparative experimental results indicate that the proposed model outperforms existing state-of-the-art methods in terms of detection performance. The proposed DDS-YOLO model achieves 59.3 % in mask mean Average Precision (mAP) and 61.3 % in box mAP, and the model parameters and computational requirements also meet practical application scenarios.

Delving Into Cluttered Prohibited Item Detection for Security Inspection System

Delving Into Cluttered Prohibited Item Detection for Security Inspection System

Risk Management Analysis Based on ISO 31000:2018 at Andi Djemma Masamba Airport, Luwu Regency

The density of activities that take place around the operating facilities at Andi Djemma Masamba Airport so that the Management of the Andi Jemma Airport Operator Unit (UPBU) Office has launched the development of Andi Djemma Masamba Airport as an aviation company so that it continues to carry out various facility improvements, especially in risk prevention with potential activities with high losses, therefore it is necessary to conduct research to find out how to map, risk assessment and design a risk control and mitigation system at the Operations facility at Andi Djemma Masamba Airport caused by activities that have the potential to cause risk. The stages of the risk management process refer to ISO 31000: 2018 including risk identification, risk analysis and risk evaluation. Expected impact and likelihood of occurrence are mapped through the risk matrix from the Federation Aviation Administration (FAA). The results showed that at the andi djemma masamba airport there were 6 events that fell into the red category, namely Aircraft accidents during take off/landing, Aircraft accidents in the apron area, Escape of dangerous goods into the aircraft, Fuel Spills on the Apron/Taxiway, Lack of building maintenance, The security inspection process is less than optimal.

A new efficient imaging reconstruction method for muon scattering tomography

Muon scattering tomography (MST) is a promising technique in nuclear security inspection, utilizing its multiple scattering property which is sensitive to elements with high atomic numbers, and is also correlated with the material density. A proper imaging reconstruction method is essential for revealing the spatial distribution of materials within an object from measurements. This work proposes a new imaging reconstruction method called “big voxel and angle capping”. The scattering angle is reconstructed using the conventional Point of Closest Approach (PoCA) method. In the new method, reconstructed scattering angles belonging to voxels in an l×m×n matrix are aggregated into the center voxel, termed the “big voxel”. This significantly reduces statistical fluctuations in each big voxel. Subsequently, the influence of large scattering angles on determining material densities in big voxels is mitigated using a capping angle. The proposed algorithm is tested on the experimental data comprising approximately 1.8×104 muon scattering events detected in ∼ 24 h. The mean square error (MSE) and the structural similarity index measure (SSIM) indices are employed to quantitatively assess the imaging quality and optimize the new method. After scanning the two major parameters, the optimal parameter space is achieved with the big voxel size of ∼ 9, and the capping angle of ∼4∘ for tungsten blocks. The imaging quality is more sensitive to the capping angle than the big voxel size. An appropriate capping angle effectively removes large artifacts in reconstructed images caused by muon events with large scattering angles. This method is efficient in terms of both time and storage consumption.

Transformer-based dual-view X-ray security inspection image analysis

Artificial intelligence technology is rapidly advancing and has been widely applied in the field of intelligent security inspection. Utilizing computer vision technology to detect prohibited items in X-ray images has drawn much attention. Due to the transmission effect of X-rays, single-view security inspection images are prone to object occlusion and poor imaging angles, which seriously affects the performance of object detection models. Dual-view security inspection equipment can simultaneously capture X-ray transmission images of the item under inspection from both horizontal and vertical angles, which can effectively address issues of poor imaging angles and object occlusions that single-view imaging cannot resolve. In this paper, we introduced the artificial intelligence technology in dual-view security inspection image analysis, and proposed the dual-view feature fusion and prohibited item detection model in X-ray security inspection images based on the Vision Transformer framework. The detection model contains two input channels: the main and secondary channel. The main function of the main channel is to detect prohibited items in security inspection images, while the secondary channel is dedicated to providing effective feature information of prohibited items for the main channel. Two feature interaction modules are applied in the proposed model to realize dual channel information exchange and supplement from local and global perspectives respectively. Simulation results based on the public Dualray dataset have demonstrated the state-of-the-art performance of the proposed dual-view X-ray image detection model. Code is available at https://github.com/zhg-SZPT/Trans2Ray.

Recognition of vehicle license plates in highway scenes with deep fusion network and connectionist temporal classification

AbstractLicense plate recognition is crucial in Intelligent Transportation Systems (ITS) for vehicle management, traffic monitoring, and security inspection. In highway scenarios, this task faces challenges such as diversity, blurriness, occlusion, and illumination variation of license plates. This article explores Recurrent Neural Networks based on Connectionist Temporal Classification (RNN‐CTC) for license plate recognition in challenging highway conditions. Four neural network models: ResNet50, ResNeXt, InceptionV3, and SENet, all combined with RNN‐CTC are comparatively evaluated. Furthermore, a novel architecture named ResNet50 Deep Fusion Network using Connectionist Temporal Classification (ResNet50‐DFN‐CTC) is proposed. Comparative and ablation experiments are conducted using the Highway License Plate Dataset of Southeast University (HLPD‐SU). Results demonstrate the superior performance of ResNet50‐DFN‐CTC in challenging highway conditions, achieving 93.158% accuracy with a processing time of 7.91 ms, outperforming other tested models. This research contributes to advancing license plate recognition technology for real‐world highway applications under adverse conditions.

The Influence of Security Check Point (SCP) 2 Security Inspection Services on Passenger Satisfaction at the Aviation Security Unit (AVSEC) of Adi Soemarmo Boyolali International Airport

The Influence of Security Check Point (SCP) 2 Security Inspection Services on Passenger Satisfaction at the Aviation Security Unit (AVSEC) of Adi Soemarmo Boyolali International Airport

Evaluation of radiation leakage in X-ray security inspection machine using a CZT spectrometer

Leakage radiation from X-ray security inspection machines is important, and measurement based on ionization chamber or scintillator detector is widely used. The leakage radiation is closely related to the size and the passing time of the luggage, the lead equivalent, the opening angle of the lead curtain, and the response time of the measuring instrument. To characterize the distribution of leakage radiation from the X-ray security inspection machine accurately, a small-volume CZT(CdZnTe) spectrometer was used to measure the energy spectra at a distance of 5 cm from the surface of the inspection machine. By designing and controlling the opening angle of the lead curtain according to the size of the luggage passing through the entrance and exit, the radiation dose was determined based on the measured energy spectra combined with the G(E)-function method. The results show that the maximum relative deviation between the air kerma rate and the ambient dose equivalent rate calculated by the G(E) function method with the standard dose rate does not exceed ±5%. The maximum relative deviation of the dose rate linear verification in the 137Cs radiation field is less than 2.5%. A calibrated CZT detector was utilized to measure the radiation leakage on the surface of the X-ray security inspection machine. It was discovered that the presence of the luggage items and the opening angle of the lead curtain will increase the leakage radiation dose on the surface of the security inspection machine system. This study provides a new approach for measuring scattered radiation of X-ray security inspection machines.

A Review of Attacker–Defender Games and Cyber Security

The focus of this review is the long and broad history of attacker–defender games as a foundation for the narrower and shorter history of cyber security. The purpose is to illustrate the role of game theory in cyber security and which areas have received attention and to indicate future research directions. The methodology uses the search terms game theory, attack, defense, and cyber security in Web of Science, augmented with the authors’ knowledge of the field. Games may involve multiple attackers and defenders over multiple periods. Defense involves security screening and inspection, the detection of invaders, jamming, secrecy, and deception. Incomplete information is reviewed due to its inevitable presence in cyber security. The findings pertain to players sharing information weighted against the security investment, influenced by social planning. Attackers stockpile zero-day cyber vulnerabilities. Defenders build deterrent resilient systems. Stochastic cyber security games play a role due to uncertainty and the need to build probabilistic models. Such games can be further developed. Cyber security games based on traffic and transportation are reviewed; they are influenced by the more extensive communication of GPS data. Such games should be extended to comprise air, land, and sea. Finally, cyber security education and board games are reviewed, which play a prominent role.

Simultaneous Electrodeposition of Gold Nanoparticles and Electropolymerization of L-Cysteine through Paper-Based Microfluidic Sensor

Introduction Using catalytic active modifiers and additives e.g. to improve electron or ion transport in electrochemical sensors usually offers significant benefits by improving sensitivity and selectivity. An elegant way to introduce both is electrodeposition and electropolymerization. Examples are known, where through these methods enhanced catalytic activity, stability, reproducibility, and active surface area could be achieved [1,2]. Paper-based microfluidic electrochemical sensors (μCS) have gained significant attention due to their simplicity, portability, cost-effectiveness, and potential for point-of-care applications. Although electrodeposition of AuNPs and electropolymerization of (L-cysteine) have been widely studied in static voltammetry using glassy carbon electrodes or screen-printed carbon-based electrodes (SPCE), their application by the microfluidic systems has been limited, and based on our knowledge, no report has been done on electrodeposition and electropolymerization through a μCS.In this work, we show the electrodeposition of AuNPs and electropolymerization of (L-cysteine) onto the electrodes of paper-based microfluidic electrochemical sensor, while using the microfluidic channel also for transport of the electrodeposition solution. Experimental The alignment of the component for the μCS device is sketched in Scheme 1. After providing the HAuCl4 and L-cysteine solution through the sponge the electrochemical fabrication and detection are carried out through cyclic voltammetry (CV). AuNPs were electrodeposited and L-cysteine were electropolymerized simultaneously through the μCS by handling a potential window of −1.5 to +2.2 V at a scan rate of 100 mV/s for 10 cycles in a solution of 0.1 M PBS at pH = 5.5 consists of 1.0 mM HAuCl4 and 1.0 mM L-cysteine. Scanning electron microscope (SEM), CV, and electrochemical impedance spectroscopy (EIS) were employed to certify that the AuNPs and poly (L-cysteine) were attached successfully to the working electrode surface. Results and discussion CVs of the platforms were recorded in the redox probe solution consisting of 5.0 mM of [Fe(CN)6]3−/4−. By modifying the platforms by simultaneous electrodeposition of AuNPs and electropolymerization of (L-cysteine) not only did peak currents increase dramatically but also ΔEp were decreased to 17 mV and 13 mV for SPCE/S (ED & EP) and μCS/S (ED & EP), respectively. This decrement behavior indicating the presence of AuNPs, and poly (L-cysteine) enhances the reversible nature of the electrochemical reaction.By comparing the Nyquist plots related to the bare SPCE and μCS it is clear that the Rct value for μCS (46 Ω) is much lower than SPCE (690 Ω) It may be attributed to the 3D and sandwich-like structure of μCS devices. SPCE/S (ED & EP) and μCS/S (ED & EP) present very smaller semicircles with Rct values of 225 Ω and 13 Ω, respectively. This remarkable increase in the current and substantial decrease in resistance of the modified platforms at first confirms which AuNPs and poly (L-cysteine) were successfully achieved onto the WE surface. Interestingly, the simultaneous deposition of AuNPs and poly (L-cysteine) by using μCS shows the lowest Rct and could be a highly interesting sensing electrode.To ensure the performance of both simultaneous electrodeoposition of AuNPs and electropolymerization of (L-cysteine) and μCS in the enhancement of the surface area, accordingly, the calculated A of the SPCE/S (ED & EP) and μCS/S (ED & EP) were estimated to be 0.09 cm2 and 1.14 cm2, respectively. In comparison with the A value of the bare SPCE (0.06 cm2) and μCS, (0.31 cm2) these increases are 1.5 times for the SPCE and 3.6 times for the μCS. Conclusion The μCS/S (ED & EP) offers several core advantages over SPCE/S (ED & EP) in terms of cost, simplicity, and sensitivity. These unique advantages are due to the combined microfluidic configuration, 3D electrode layout, and a unique electrochemical modifier. According to the SEM results, the images corroborated that the simultaneous synthesis of nanocomposite on the working electrode surface is carried out successfully. This strategy can assist as the proper way to boost the surface area of the sensor. We believe our findings would have significant implications in developing other portable, fast, and cost-effective electrochemical detection platforms, such as clinical diagnosis and security inspection. References Mei, Xue, et al. "A novel electrochemical sensor based on gold nanobipyramids and poly-l-cysteine for the sensitive determination of trilobatin." Analyst 148.10 (2023): 2335-2342.Ghanbari, Mohammad Hossein, et al. "Simultaneous electrochemical detection of uric acid and xanthine based on electrodeposited B, N co-doped reduced graphene oxide, gold nanoparticles and electropolymerized poly (L-cysteine) gradually modified electrode platform." Microchemical Journal 175 (2022): 107213.Bard, Allen J., Larry R. Faulkner, and Henry S. White. Electrochemical methods: fundamentals and applications. John Wiley & Sons, 2022. Figure 1

Terahertz super-resolution imaging with large depth of field enabled by the terajet of bilayer dielectric spheres

Terahertz imaging has found extensive applications in non-destructive testing, security inspection, and other various fields. Intensive research on terahertz imaging systems has been executed to pursue high performance on imaging resolution and depth of field (DOF). However, the terahertz imaging systems with both high imaging resolution and large DOF have rarely been reported. In this paper, a mesoscopic-sized dielectric bilayer sphere-assisted super-resolution imaging method was proposed to simultaneously achieve enormously improved imaging resolution and extended DOF. Simulation analyses revealed that the ultrathin and long terajets were generated by the well-designed bilayer sphere. The THz super-resolution image for the samples can be captured by the point-by-point terajet scanning. The experimental results demonstrated that the best resolution reached up to 0.4λ, the DOF with super-resolution was up to 2λ, and the DOF with sub-wavelength resolution was up to 4λ. This method holds great potential for widespread application in terahertz imaging and detection, especially for curved or complex sample structures.

Towards large-scale single-shot millimeter-wave imaging for low-cost security inspection

Millimeter-Wave (MMW) imaging is a promising technique for contactless security inspection. However, the high cost of requisite large-scale antenna arrays hinders its widespread application in high-throughput scenarios. Here, we report a large-scale single-shot MMW imaging framework, achieving low-cost high-fidelity security inspection. We first analyzed the statistical ranking of each array element through 1934 full-sampled MMW echoes. The highest-ranked elements are preferentially selected based on the ranking, building the experimentally optimal sparse sampling strategy that reduces antenna array cost by one order of magnitude. Additionally, we derived an untrained interpretable learning scheme, realizing robust and accurate MMW image reconstruction from sparsely sampled echoes. Last, we developed a neural network for automatic object detection, and experimentally demonstrated successful detection of concealed centimeter-sized targets using 10% sparse array, whereas all the other contemporary approaches failed at such a low sampling ratio. With the strong detection ability and order-of-magnitude cost reduction, we anticipate that this technique provides a practical way for large-scale single-shot MMW imaging.

Concealed hazardous object detection for terahertz images with cross-feature fusion transformer

Terahertz (THz) waves can penetrate many non-metallic materials (such as paper, plastic, and clothing), making them highly valuable in security inspection areas. However, the low contrast between target objects and the background in THz images poses problems for object detection and recognition tasks. Though many schemes have been proposed to tackle the problems, the low detection accuracy and high computational complexity remain challenges. To address these challenges, we propose a concealed hazardous object detection method called Cross-Feature Fusion Transformer YOLO (CFT-YOLO) for THz images. We design a Multi-Channel Spatial Feature Module (MCSFM) to achieve cross-channel and cross-spatial information transfer between feature maps. MCSFM also realizes the feature parameter transfer between the backbone and neck of the network. Additionally, we develop a CrossFuse-Transformer (CF-Former) module that leverages self-attention to fully exploit the correlations and structural information between different feature maps. We conduct a series of experiments on public datasets. The results show that CFT-YOLO outperforms the other advanced methods in THz hazardous object detection tasks. Moreover, the CFT-YOLO achieves a better balance between model complexity and detection accuracy compared to YOLOv8s, making it more practical for real-world applications.

Enhancing port security in Taiwan by establishing security inspection procedure to combat Fisheries-Related Crimes

Fisheries-Related Crimes (FRC) pose a significant threat to any country’s national security, with illicit activities often concealed within the expansive and seemingly innocuous domain of fishing vessels. This paper explores the need to strengthen port inspection protocols to counteract illegal activities utilizing fishing vessels as conduits. The fishing vessel Inspection Criteria (ICs) were developed based on port inspection features and relevant literature. The Best-Worse Method (BWM) is then used to weigh those ICs, creating a new inspection checklist. Finally, based on the checklist, an improved inspection procedure is proposed. The results may provide government authorities and policymakers with practical information on effectively and efficiently conducting port security inspections on fishing vessels and preventing FRCs.

Contraband detection method in X-ray image based on improved CenterNet network

Security inspection has been played a crucial role in ensuring the safety of railway passenger transportation. The detection of contraband in X-ray images is an important part of safety inspection work. Since the X-ray image of security inspection is a pseudo color image with a unique imaging style, and the restricted objects are mostly small targets, the accuracy of the object detection algorithm using visible light images directly is not high. This research will propose an improved CenterNet method, and design a 16 times down sampling rate backbone network, which is more suitable for detecting contraband in X-ray images. This article will introduce attention mechanisms to increase attention to effective and key areas in X-ray images, and reveal the mechanisms of spatial and channel attention in X-ray images. Furthermore, by designing an adaptive feature fusion mechanism, the drawback of the original CenterNet by using only the last layer of output features is compensated. Experiments have shown that on the SIXray dataset, mAP is improved by 3.3% compared to the original CenterNet method, and 5.5%, 2.4%, 3.6%, 10.1% compared to SSD, Yolo V3, FPN, DERT algorithms. The detection accuracy of restricted items in X-ray images is effectively improved.

One-Dimensional Copper-Doped Rb2AgI3 with Efficient Sky-Blue Emission as a High-Performance X-ray Scintillator.

Scintillators have garnered heightened attention for their diverse applications in medical imaging and security inspection. Nonetheless, commercial scintillators encounter challenges with costly rare-earth metals and toxic elements like thallium (Tl), driving the need for sustainable, cost-effective, and eco-friendly alternatives to meet contemporary X-ray detection demands. This study focuses on exploring the potential of Cu+-doped Rb2AgI3 as an effective metal halide (MH) scintillator. One-dimensional (1D) Rb2AgI3 and Cu+-doped Rb2AgI3 single crystals (SCs) were synthesized by using the conventional temperature-lowering crystallization method. When excited by UV light, Cu+-doped SCs emitted a broad sky-blue light at 490 nm with a high photoluminescence (PL) quantum yield (PLQY) of 76.48%. Remarkably, under X-ray excitation, these Cu+-doped SCs demonstrated an outstanding light yield of 36,293 photons MeV-1, a relatively low detection threshold of 1.022 μGyair s-1, and a rapid scintillation decay time of 465 ns. The prepared translucent scintillation film has good uniformity and flexibility, with a high spatial resolution of 10.2 lp mm-1. These results position Cu+-doped Rb2AgI3 as a leading candidate among promising X-ray scintillators, offering superior scintillation light yield, excellent stability, and nontoxicity.