In vitro metabolic studies and machine learning analysis of mass spectrometry data: A dual strategy for differentiating alpha-pyrrolidinohexiophenone (α-PHP) and alpha-pyrrolidinoisohexanophenone (α-PiHP) in urine analysis.

Synthetic cathinones are some of the most prevalent new psychoactive substances (NPSs) globally, with alpha-pyrrolidinoisohexanophenone (α-PiHP) being particularly noted for its widespread use in the United States, Europe, and Taiwan. However, the analysis of isomeric NPSs such as α-PiHP and alpha-pyrrolidinohexiophenone (α-PHP) is challenging owing to similarities in their retention times and mass spectra. This study proposes a dual strategy based on in vitro metabolic experiments and machine learning-based classification modelling for differentiating α-PHP and α-PiHP in urine samples: (1) in vitro metabolic experiments using pooled human liver microsomes and liquid chromatography tandem quadrupole time-of-flight mass spectrometry (LC-QTOF-MS) were conducted to identify the key metabolites of α-PHP and α-PiHP from the high-resolution MS/MS spectra. After 5 h incubation, 71.4 % of α-PHP and 64.7 % of α-PiHP remained unmetabolised. Nine phase I metabolites were identified for each compound, including primary ß-ketone reduction (M1) metabolites. Comparing the metabolites and retention times confirmed the efficacy of in vitro metabolic experiments for differentiating NPS isomers. Subsequently, analysis of seven real urine samples revealed the presence for various metabolites, including M1, that could be used as suitable detection markers at low concentrations. The aliphatic hydroxylation (M2) metabolite peak counts and metabolite retention times were used to determine α-PiHP use. (2) Classification models for the parent compounds and M1 metabolites were developed using principal component analysis for feature extraction and logistic regression for classification. The training and test sets were devised from the spectra of standard samples or supernatants from in vitro metabolism experiments with different incubation times. Both models had classification accuracies of 100 % and accurately identified α-PiHP and its M1 metabolite in seven real urine samples. The proposed methodology effectively distinguished between such isomers and confirmed their presence at low concentrations. Overall, this study introduces a novel concept that addresses the complexities in analysing isomeric NPSs and suggests a path towards enhancing the accuracy and reliability of NPS detection.

A forensic examination of four popular cross-platform file-sharing apps with Wi-Fi P2P.

File-sharing apps with Wi-Fi hotspot or Wi-Fi Direct functions become more popular. They can work on multiple platforms and allow users to transfer files in a concealed manner. However, when criminals use these apps in illegal activities, it becomes an important issue for investigators to find digital evidence on multiple platforms. At present, there are few studies on this topic, and most of them are limited to the single platform problem. In this paper, we propose a forensic examination method for four popular cross-platform file-sharing apps with Wi-Fi hotspot and Wi-Fi Direct functions: Zapya, SHAREit, Xender, and Feem. We use 22 static and live forensic tools for 11 platforms to acquire, analyze, and classify the forensic artifacts. In our experiments, we find many useful forensic artifacts and classify them into six categories. The experimental results can support law enforcement investigations of digital evidence and provide information for future studies on other cross-platform file-sharing apps.

Quantitative assessment of allergic shiners in children with allergic rhinitis.

BACKGROUND: The knowledge on allergic shiners is extremely limited. A conceivable tool able to quantify allergic shiners has not been established. OBJECTIVES: We sought to determine the significance and changeability of allergic shiners through our newly developed computerized method. METHODS: We developed a novel computerized method to measure allergic shiners and enrolled a cohort of children with or without allergic rhinitis. Children with allergic rhinitis were prospectively assessed. A standardized digital photograph was taken during each visit, and a modified Pediatric Rhinoconjunctivitis Quality of Life Questionnaire was completed. Subject global assessment for nose symptoms and subject global assessment for eye symptoms (SGAE) were self-recorded daily. RESULTS: We included 126 children with allergic rhinitis and 123 healthy control subjects. One hundred three (82%) participants with allergic rhinitis completed at least 4 prospective assessments. Shiners were darker (P < .001) and larger (P < .001) in children with allergic rhinitis. Darkness and sizes of allergic shiners were paradoxically inversely correlated (P = .02). Darkness of allergic shiners positively correlated with the duration of allergic rhinitis, practical problem scores, and SGAE values (P = .02, P = .004, and P = .002, respectively), but sizes of allergic shiners did not. Shiners were found to be darker in children with scores of eye symptoms of greater than 6, scores of practical problems of greater than 5, and SGAE values of greater than 0 (P = .02, P < .001, and P = .003, respectively), whereas shiners were larger in children with scores of other symptoms of greater than 9 and activity limitations of greater than 4 (P = .02 and P = .002, respectively). CONCLUSION: Computer-analyzed allergic shiners correlate with the chronicity and severity of allergic rhinitis.

A motion detection-based framework for improving image quality of CCTV security systems.

Closed-circuit television (CCTV) security systems have been widely used in banks, convenience stores, and other facilities. They are useful to deter crime and depict criminal activity. However, CCTV cameras that provide an overview of a monitored region can be useful for criminal investigation but sometimes can also be used for object identification (e.g., vehicle numbers, persons, etc.). In this paper, we propose a framework for improving the image quality of CCTV security systems. This framework is based upon motion detection technology. There are two cameras in the framework: one camera (camera A) is fixed focus with a zoom lens for moving-object detection, and the other one (camera B) is variable focus with an auto-zoom lens to capture higher resolution images of the objects of interest. When camera A detects a moving object in the monitored area, camera B, driven by an auto-zoom focus control algorithm, will take a higher resolution image of the object of interest. Experimental results show that the proposed framework can improve the likelihood that images obtained from stationary unattended CCTV cameras are sufficient to enable law enforcement officials to identify suspects and other objects of interest.

Pistol image retrieval by shape representation.

Databases have been used to record data in forensic science, such as fingerprints, shoeprints, and photos. In traditional databases, we use "text" as the "keyword" for retrieving data (text-keyword retrieval); however, in some applications, "text" is not proper to describe the searching target, and in this case, "image" plays an important role. In this paper, we use "image features" as the "keywords", and show its potential for building up a prototype of pistol image databases. In current firearm databases of forensic science, the retrieval method is still by text-keyword retrieval. For experienced forensic examiners, this kind of databases may satisfy their requirement. However, for people who have little gun knowledge, how can they find the possible gun candidates or similar guns in the databases, if there are not any available words or marks on the gun? In this paper, we try to retrieve similar pistol images by the pistol shape instead of "text-keyword". This method can narrow down the searching range while identifying pistols by firearm databases. There are more than 300 pistol images in our pistol image database. From the experimental results, we can retrieve the similar pistol images in top five candidates for each pistol image.

The mask detection technology for occluded face analysis in the surveillance system.

The surveillance systems have been widely used in automatic teller machines (ATMs), banks, convenient stores, etc. For example, when a customer uses the ATM, the surveillance systems will record his/her face information. The information will help us understand and trace who withdrew money. However, when criminals use the ATM to withdraw illegal money, they usually block their faces with something (in Taiwan, criminals usually use safety helmets or masks to block their faces). That will degrade the purpose of the surveillance system. In previous work, we already proposed a technology for safety helmet detection. In this paper, we propose a mask detection technology based upon automatic face recognition methods. We use the Gabor filters to generate facial features and utilize geometric analysis algorithms for mask detection. The technology can give an early warning to save-guards when any "customer" or "intruder" blocks his/her face information with a mask. Besides, the technology can assist face detection in the automatic face recognition system. Experimental results show the performance and reliability of the proposed technology.

The safety helmet detection technology and its application to the surveillance system.

The Automatic Teller Machine (ATM) plays an important role in the modem economy. It provides a fast and convenient way to process transactions between banks and their customers. Unfortunately, it also provides a convenient way for criminals to get illegal money or use stolen ATM cards to extract money from their victims' accounts. For safety reasons, each ATM has a surveillance system to record customer's face information. However, when criminals use an ATM to withdraw money illegally, they usually hide their faces with something (in Taiwan, criminals usually use safety helmets to block their faces) to avoid the surveillance system recording their face information, which decreases the efficiency of the surveillance system. In this paper, we propose a circle/circular arc detection method based upon the modified Hough transform, and apply it to the detection of safety helmets for the surveillance system of ATMs. Since the safety helmet location will be within the set of the obtainable circles/circular arcs (if any exist), we use geometric features to verify if any safety helmet exists in the set. The proposed method can be used to help the surveillance systems record a customer's face information more precisely. If customers wear safety helmets to block their faces, the system can send a message to remind them to take off their helmets. Besides this, the method can be applied to the surveillance systems of banks by providing an early warning safeguard when any "customer" or "intruder" uses a safety helmet to avoid his/her face information from being recorded by the surveillance system. This will make the surveillance system more useful. Real images are used to analyze the performance of the proposed method.

Fingerprint pattern restoration by digital image processing techniques.

Fingerprint evidence plays an important role in solving criminal problems. However, defective (lacking information needed for completeness) or contaminated (undesirable information included) fingerprint patterns make identifying and recognizing processes difficult. Unfortunately. this is the usual case. In the recognizing process (enhancement of patterns, or elimination of "false alarms" so that a fingerprint pattern can be searched in the Automated Fingerprint Identification System (AFIS)), chemical and physical techniques have been proposed to improve pattern legibility. In the identifying process, a fingerprint examiner can enhance contaminated (but not defective) fingerprint patterns under guidelines provided by the Scientific Working Group on Friction Ridge Analysis, Study and Technology (SWGFAST), the Scientific Working Group on Imaging Technology (SWGIT), and an AFIS working group within the National Institute of Justice. Recently, the image processing techniques have been successfully applied in forensic science. For example, we have applied image enhancement methods to improve the legibility of digital images such as fingerprints and vehicle plate numbers. In this paper, we propose a novel digital image restoration technique based on the AM (amplitude modulation)-FM (frequency modulation) reaction-diffusion method to restore defective or contaminated fingerprint patterns. This method shows its potential application to fingerprint pattern enhancement in the recognizing process (but not for the identifying process). Synthetic and real images are used to show the capability of the proposed method. The results of enhancing fingerprint patterns by the manual process and our method are evaluated and compared.

A 3-D transformation to improve the legibility of license plate numbers.

In this paper, a novel three-dimensional transformation method for vehicle license plate number recognition is proposed. This method provides an efficient solution to normalize skew distorted vehicle license plate images. The Hough transform is used to estimate the license plate position and the normalization angle. After the three-dimensional transformation and normalization processes, the vehicle license plate numbers are recognized easily. Real vehicle license plate images are used to show the capability of the proposed method. The provided method is also useful for other skewed writings, such as the text printed on a suspect's shirt.