Table 10 - uploaded by Alessandro Ulrici
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(1) Background: Police forces and security administrations are nowadays considering Improvised explosives (IEs) as a major threat. The chemical substances used to prepare IEs are called precursors, and their presence could allow police forces to locate a bomb factory where the on-going manufacturing of IEs is carried out. (2) Methods: An expert sys...
Citations
... Clandestine laboratory investigation is one of the most dangerous tasks undertaken by law enforcement due to the presence of hazardous chemical compounds [2]. A "bomb factory" cannot be immediately distinguished from a clandestine laboratory preparing drugs of abuse, despite the presence in the scientific literature of analytical approaches to spot bomb factories [6][7][8][9][10]. ...
Illicit drug production in clandestine laboratories involves the use of large quantities of different chemicals that can be obtained for legitimate purposes. The identification of these chemicals, including reagents, catalyzers and solvents, is crucial for forensic investigations. From a legal point of view, a drug precursor is a material that is specific and critical to the production of a finished chemical and that constitutes a significant portion of the final molecular structure of the drug. In this study, a gas chromatography quartz-enhanced photoacoustic spectroscopy (GC-QEPAS) sensor—in conjunction with a deep learning model—was evaluated for its effectiveness in the detection and identification of interesting compounds for the production of amphetamine, methamphetamine, 3,4-methylenedioxymethamphetamine (MDMA), phenylcyclohexyl piperidine (PCP), and cocaine. The GC-QEPAS sensor includes a gas sampler, a fast GC for separation, and a QEPAS detector, which excites molecules exiting the GC column using a quantum cascade laser to provide the infra-red (IR) spectrum. The on-site capability of the GC-QEPAS system offers significant advantages over the current instruments employed in this field, including rapid analysis, which is crucial in field operations. This allows law enforcement to quickly identify specimens of interest on site. The system’s performance was validated by taking into account the limit of detection, repeatability, and within-laboratory reproducibility. The results showed excellent repeatability and reproducibility for both the GC and QEPAS modules. The deep learning model, a multilayer perceptron neural network, was trained using IR spectra and retention times, achieving very high classification accuracy in the testing conditions. This study demonstrated the efficacy of the GC-QEPAS sensor combined with a deep learning model for the reliable identification of drug precursors, providing a robust tool for law enforcement during criminal investigations in clandestine laboratories.
... This component also ensures data security and integrity of all connected data. It is further described below; The Data-Fusion and Alerting Component that processes and correlates complementary and orthogonal analytical information from multiple sensors, by means of an expert system for pattern recognition on the data collected by each sensor of a network, estimating the probability of each target by each sensor (Ferrari, Ulrici and Romolo, 2017). Alerts are generated based on pre-defined conditions. ...
Following a chemical or biological attack, the rate at which a forensic investigation proceeds is critical for capturing perpetrators and preventing future incidents. Crime Scene Investigation (CSI) is a process that aims at recording the scene as it is first encountered and recognizing and collecting all the physical evidence potentially relevant to the solution of the case. Conventional CSI processes involve sensitive and delicate steps related with the identification and transportation of traces that may subsequently be subjected to laboratory analysis, a process that can take several hours or days.
In this paper, we present the Real-tIme on-site forenSic tracE qualificatioN (RISEN) project, an innovate concept in forensic investigations in the context of CSI of sites affected by a chemical or biological attack. Coordinated by ENEA, RISEN will develop a set of network- enabled real-time contactless sensors for handling traces on site and accurate 3D recreation mechanisms of the entire crime scene, providing an immersive environment for investigators to evaluate hypotheses and conduct highly detailed investigations. The RISEN concept will allow forensics investigators and judicial authorities, to gather high quality information from a vast list of visible and invisible traces (localisation, identification/classification, interpretation and labelling) from a crime scene through standardised reports and a secure way, also speeding- up the forensic investigation process.
The RISEN project started in July 2020 and has a duration of 4 years.
RISEN public information can be assessed at the project website https://www.risen-h2020.eu/.
... [36][37][38] The multi-sensor system consists of several optical techniques including surface-enhanced Raman spectroscopy (SERS), quartz-enhanced photoacoustic spectroscopy (QEPAS), electrochemical (EC) sensing, a light detection and ranging (LIDAR)/differential absorption LIDAR detection system (DIAL) sensor. 39 The COSMIC project, due to be completed in 2021, aims to develop new screening technologies for the detection of Chemical, Biological, Radiological, Nuclear and Explosive (CBRNE) materials concealed in shipping containers. 40 The NATOfunded STANDEX (STANdoff Detection of EXplosives) program is another example of an integrated early-warning system, for deployment in mass transit infrastructure locations. ...
Explosive trace detection (ETD) technologies play a vital role in maintaining national security. ETD remains an active research area with many analytical techniques in operational use. This review details the latest developments in animal olfactory, ion mobility spectrometry (IMS), Raman and colorimetric detection methods. Developments in optical, biological, electrochemical, mass and thermal sensors are also covered in addition to the use of nanomaterials technology. Commercially available systems are presented as examples of current detection capabilities and as benchmarks for improvement. Attention is also drawn to recent collaborative projects involving government, academia and industry, to highlight the emergence of multi-modal screening approaches and applications. The objective of the review is to provide a comprehensive overview of ETD by; highlighting challenges in ETD, providing an understanding of the principles, advantages and limitations of each technology and relating this to current systems.
... Narcotics and narcotics residue in sewage might be measured to get an indicator of drug use in cities [99]. In the near future, sensor systems might even enable law enforcement agencies to detect the precursors of improvised bombs in the sewage system, which might allow these agencies to dismantle bomb laboratories [100]. ...
Global society is confronted with various challenges: climate change should be mitigated, and society should adapt to the impacts of climate change; resources will become scarcer and hence resources should be used more efficiently and recovered after use; the growing world population and its growing wealth create unprecedented emissions of pollutants, threatening public health, wildlife and biodiversity. This paper provides an overview of the challenges and risks for sewage systems, next to some opportunities and chances that these developments pose. Some of the challenges are emerging from climate change and resource scarcity, others come from the challenges emerging from stricter regulation of emissions. It also
presents risks and threats from within the system, next to external influences which may affect the surroundings of the sewage systems. It finally reflects on barriers to respond to these challenges.
... Real condition measurements were carried out with the following operational setup composed of: Figure 2A) connected through GSM (Global System for Mobile Communication) to the central control system (using TEKEVER communication tool box [22]). • ...
... This setup enabled the electrode array to be immersed in flushing water. [22]). ...
Although all countries are intensifying their efforts against terrorism and increasing their mutual cooperation, terrorist bombing is still one of the greatest threats to society. The discovery of hidden bomb factories is of primary importance in the prevention of terrorism activities. Criminals preparing improvised explosives (IE) use chemical substances called precursors. These compounds are released in the air and in the waste water during IE production. Tracking sources of precursors by analyzing air or wastewater can then be an important clue for bomb factories’ localization. We are reporting here a new multiplex electrochemical sensor dedicated to the on-site simultaneous detection of three explosive precursors, potentially used for improvised explosive device preparation (hereafter referenced as B01, B08, and B15, for security disclosure reasons and to avoid being detrimental to the security of the counter-explosive EU action). The electrochemical sensors were designed to be disposable and to combine ease of use and portability in a screen-printed eight-electrochemical cell array format. The working electrodes were modified with different electrodeposited metals: gold, palladium, and platinum. These different coatings giving selectivity to the multi-sensor through a “fingerprint”-like signal subsequently analyzed using partial least squares-discriminant analysis (PLS-DA). Results are given regarding the detection of the three compounds in a real environment and in the presence of potentially interfering species.
Achieving fast analytical results on-site with the highest possible accuracy in forensic analyses is crucial for investigations. While portable sensors are essential for crime scene analysis, they often face limitations in sensitivity and specificity, especially due to environmental factors. Data fusion (DF) techniques can enhance accuracy and reliability by combining information from multiple sensors. This study develops different DF approaches using data from two sensors: ion mobility spectrometry (IMS) and gas chromatography-quartz-enhanced photoacoustic spectroscopy (GC-QEPAS), aiming to improve the safety of crime scene operators and the accuracy of on-site forensic analysis. Two DF approaches were developed for acetone and DMMP: low-level (LLDF) and mid-level (MLDF), meanwhile a high-level (HLDF) approach was applied to TATP. LLDF concatenated preprocessed data matrices, while MLDF employed principal component analysis for feature extraction. LLDF and MLDF used one-class support vector machines (OC-SVM) for classification, while HLDF combined OC-SVM for IMS and SIMCA for GC-QEPAS. Sensor location within crime scenes was established using traditional measuring tape and laser distance meters, with a 1 m cutoff distance between sensors deemed appropriate for indoor crime scenes. LLDF achieved high accuracy but was sensitive to concentration variations, while MLDF enhanced the classification robustness. HLDF allowed for independent sensor use in real scenarios. All of the methods reached 100% accuracy for DMMP and acetone, and the MLDF approach was the fastest among the DF methods, demonstrating its potential for rapid applications. DF approaches can significantly enhance the safety and accuracy of forensic investigations, with future research planned to extend data sets and include more sensors.
Warfare threats and acts of terror are challenging situations encountered by defense agencies across the globe and are of growing concern to the general public, and security-minded policy makers. Detecting ultra-low quantities of explosive compounds in remote locations or under harsh conditions for anti-terror purposes as well as the environmental monitoring of residual or discarded explosives in soil, remains a major challenge. The use of metal nanoparticles (NPs) for trace explosive detection has drawn considerable interest in recent years. For nano-based explosive sensor devices to meet real-life operational demands, analytical parameters such as, long-shelf life, stability under harsh conditions, ease-of-use, high sensitivity, excellent selectivity, and rapid signal response must be met. Generally, the analytical performance of colorimetric-based nanosensor systems is strongly dependent on the surface properties of the nanomaterial used in the colorimetric assay. The size and shape properties of metal NPs, surface functionalisation efficiency, and assay fabrication methods, are factors that influence the efficacy of colorimetric explosive nanosensor systems. This review reports on the design and analytical performances of colorimetric explosive sensor systems using metal NPs as optical signal transducers. The challenges of trace explosive detection, advances in metal NP colorimetric explosive design, limitations of each methods, and possible strategies to mitigate the problems are discussed.
Driver pose estimation is a key component in driver monitoring systems, which is helpful for driver anomaly detection. Compared with traditional human pose estimation, driver pose estimation is required to be fast and compact for embedded systems. We propose fast and compact driver pose estimation that is composed of ShuffleNet V2 and integral regression. ShuffleNet V2 can reduce computational expense, and integral regression reduce quantization error of heat maps. If a driver suddenly gets seriously ill, the head of the driver is out of view. Therefore, in addition to localizing body parts, classifying whether each body part is out of view is also crucial for driver anomaly detection. We also propose a novel model which can localize and detect each body part of the driver at once. Extensive experiments have been conducted on a driver pose estimation dataset recorded with near infrared camera which can capture a driver at night. Our method achieves large improvement compared to the state-of-the-art human pose estimation methods with limited computation resources. Futhermore, We perform an ablation study of our method which composed of ShuffleNet V2, integral regression, and driver body parts detection. Finally, we show experimental results of each driver action for driver monitoring systems.
This review paper covers the forensic-relevant literature for the analysis and detection of explosives and explosives residues from 2016-2019 as a part of the 19th Interpol International Forensic Science Managers Symposium. The review papers are also available at the Interpol website at: https://www.interpol.int/Resources/Documents#Publications
During last decades, the forensic opportunity to detect and identify explosives became more and more important both to protect the safety of citizens and to support the investigations against terrorists and organised crime. The analytical chemistry of explosives has a long tradition of spot test and more traditional approaches, such as chromatography, but has also new tools, such as electro-optical ones, allowing both point detection and remote sensing. In this chapter, four spectroscopic laser based techniques are presented highlighting working principles and capabilities in discriminating explosive compounds at trace level, in field operation, locally or remotely. For each techniques, the detection limits and drawbacks are reported in the application to trace sensing. Such electro-optics tools do not aim to replace the traditional laboratory methods, rather to support them in security applications and in narrowing the area under investigation, reducing the number of samples selected for laboratory analysis. More traditional approaches are then presented and discussed to illustrate the latest development with respect to on-site testing, sampling and analysis by chromatography, electrophoresis and mass spectrometry.
