Forensic laboratories in the United States had an estimated backlog of 570,100 requests for all forensic services at the end of 2014. Latent print requests represented approximately 12% of that total backlog [1, NIJ 2019]. With ever-increasing demands and backlogs, a review of laboratory or section practices becomes vital to operations. Work process and business practice changes can increase efficiencies and result in the reduction of casework backlogs and turnaround times. The automated fingerprint identification system (AFIS) deferral policy implemented by the Latent Print Comparative Section (LPCS) of the Phoenix Police Department Laboratory Services Bureau (PPD LSB) was employed to address the latent print backlog. Five years of multiple AFIS request types were analyzed to demonstrate the positive effects and benefits of such policies, including a 26.32% decrease in turnaround time over the data collection and analysis period and a 90.96% reduction in backlogged requests for one year.

In this paper, we propose an innovative approach to improve the performance of an Automatic Fingerprint Identification System (AFIS). The method is based on the design of a Possibilistic Fingerprint Quality Assessment (PFQA) filter where ground truths of fingerprint images of effective and ineffective quality are built by learning. The first approach, QS_I, is based on the AFIS decision for the image without considering its paired image to decide its effectiveness or ineffectiveness. The second approach, QS_PI, is based on the AFIS decision when considering the pair (effective image, ineffective image). The two ground truths (effective/ineffective) are used to design the PFQA filter. PFQA discards the images for which the AFIS does not generate a correct decision. The proposed intervention does not affect how the AFIS works but ensures a selection of the input images, recognizing the most suitable ones to reach the AFIS\'s highest recognition rate (RR). The performance of PFQA is evaluated on two experimental databases using two conventional AFIS, and a comparison is made with four current fingerprint image quality assessment (IQA) methods. The results show that an AFIS using PFQA can improve its RR by roughly 10% over an AFIS not using an IQA method. However, compared to other fingerprint IQA methods using the same AFIS, the RR improvement is more modest, in a 5-6% range.

Variations in biometric parameters such as fingerprints between populations, genders and even twins are wide-ranging issues routinely examined by the forensic community. In this study we tested whether fingerprint examiners can detect if finger and palm prints originate from siblings. In total, 410 finger records and 300 palm records of siblings and non-siblings were examined by seven certified forensic fingerprint examiners with different levels of qualifications and experience. The examiners were asked to determine, based on friction ridge characteristics, if they could detect similarities to such a degree that it was possible to declare a familial relationship (FR) between two prints. The results indicated that among all seven fingerprint examiners, \'true negative\' values were very high (95-98%), meaning that in cases where FRs were absent, the examiners\' decision was usually correct. In cases where FR was declared by the expert, the probability of a \'true positive\' was 25 and 10 times higher than the probability of a \'false positive\' for finger prints and palm prints, respectively. We attempt to elucidate the specific fingerprint parameters which facilitate better sibling detection, and conclude that sibling detection by fingerprint similarity may have potential as a novel forensic tool that can be used for intelligence operations.

The development of automated fingerprint identification systems (AFIS) revolutionized casework in the friction ridge community. Through the evolution of AFIS development, algorithms have become increasingly sensitive and specific. Internal validation studies conducted by the Federal Bureau of Investigation Laboratory Division\'s Latent Print Units (LPU) assessed the performance of the Next Generation Identification (NGI) system\'s latent print matching algorithm under the various encoding methods of the Multi-Biometric Identification System (MBIS) platform for the purpose of casework implementation. Based on the results of these studies, casework practices in the LPU have become more streamlined and efficient. The increase in accuracy of the latent print matching algorithm has reduced the number of candidates compared per search. For fingerprints, the results showed the best indicator for the correct mate being the number one candidate was the difference in score between the first and second candidate. When the score difference was 1200 or higher, the first candidate was mated in 99.3% of the trials. This understanding may allow for additional quality assurance measures to be implemented in casework.

The study of A-genome Asian cotton as a potential fiber donor in Gossypium species may offer an enhanced understanding of complex genetics and novel players related to fiber quality traits. Assessment of individual fibers providing classified fiber quality information to the textile industry is Advanced Fiber Information System (AFIS) in the recent technological era. Keeping the scenario, a diverse collection of 215 Asiatic cotton accessions were evaluated across three agro-ecological zones of China. Genome-Wide Association Studies (GWAS) was performed to detect association signals related to 17 AFIS fiber quality traits grouped into four categories viz: NEPs, fiber length, maturity, and fineness. Significant correlations were found within as well as among different categories of various traits related to fiber quality. Fiber fineness has shown a strong correlation to all other categories, whereas these categories are shown interrelationships via fiber-fineness. A total of 7,429 SNPs were found in association with 17 investigated traits, of which 177 were selected as lead SNPs. In the vicinity of these lead SNPs, 56 differentially expressed genes in various tissues/development stages were identified as candidate genes. This compendium connecting trait-SNP-genes may allow further prioritization of genes in GWAS loci to enable mechanistic studies. These identified quantitative trait nucleotides (QTNs) may prove helpful in fiber quality improvement in Asian cotton through marker-assisted breeding as well as in reviving eroded genetic factors of G. hirsutum via introgression breeding.

The ongoing upgrade of AFIS worldwide resulted in new achievements in the domain of fingerprint identification. Two cases are presented which depict the importance of collecting and employing a broader search in AFIS to include lateral palm prints and non-distal phalanges prints.

Automatic Latent Fingerprint Identification Systems (AFIS) are most widely used by forensic experts in law enforcement and criminal investigations. One of the critical steps used in automatic latent fingerprint matching is to automatically extract reliable minutiae from fingerprint images. Hence, minutiae extraction is considered to be a very important step in AFIS. The performance of such systems relies heavily on the quality of the input fingerprint images. Most of the state-of-the-art AFIS failed to produce good matching results due to poor ridge patterns and the presence of background noise. To ensure the robustness of fingerprint matching against low quality latent fingerprint images, it is essential to include a good fingerprint enhancement algorithm before minutiae extraction and matching. In this paper, we have proposed an end-to-end fingerprint matching system to automatically enhance, extract minutiae, and produce matching results. To achieve this, we have proposed a method to automatically enhance the poor-quality fingerprint images using the \"Automated Deep Convolutional Neural Network (DCNN)\" and \"Fast Fourier Transform (FFT)\" filters. The Deep Convolutional Neural Network (DCNN) produces a frequency enhanced map from fingerprint domain knowledge. We propose an \"FFT Enhancement\" algorithm to enhance and extract the ridges from the frequency enhanced map. Minutiae from the enhanced ridges are automatically extracted using a proposed \"Automated Latent Minutiae Extractor (ALME)\". Based on the extracted minutiae, the fingerprints are automatically aligned, and a matching score is calculated using a proposed \"Frequency Enhanced Minutiae Matcher (FEMM)\" algorithm. Experiments are conducted on FVC2002, FVC2004, and NIST SD27 latent fingerprint databases. The minutiae extraction results show significant improvement in precision, recall, and F1 scores. We obtained the highest Rank-1 identification rate of 100% for FVC2002/2004 and 84.5% for NIST SD27 fingerprint databases. The matching results reveal that the proposed system outperforms state-of-the-art systems.

Scholarship on the latent print comparison process has expanded in recent years, responsive to the call for rigorous research by scholarly groups (e.g., National Academy of Sciences, 2009; President\'s Council of Advisors on Science and Technology, 2016). Important to the task of ultimately improving accuracy, consistency, and efficiency in the field is understanding different workflows and case outcomes. The current study describes the casework completed by a latent print unit in a large laboratory during one calendar year (2018), including a unique workflow that involves Preliminary AFIS Associations reported out as investigative leads. Approximately 45% of all examined prints were deemed to be of sufficient quality to enter into AFIS, and 22% of AFIS entries resulted in potential identifications. But examiner conclusions and AFIS outcomes (across three AFIS databases) varied according to case details, print source, and AFIS database. Moreover, examiners differed in case processing, sufficiency determinations, and AFIS conclusions. Results are discussed with respect to implications for future research (e.g., comparing these data to case processing data for other laboratories) and ultimately improving the practice of latent print examination.

Fingermarks that have insufficient characteristics for identification often have discernible characteristics that could form the basis for lesser degrees of correspondence or probability of occurrence within a population. Currently, those latent prints that experts judge to be insufficient for identification are not used as associative evidence. How often do such prints occur and what is their potential value for association? The answers are important. We could be routinely setting aside a very important source of associative evidence, with high potential impact, in many cases; or such prints might be of very low utility, adding very little, or only very rarely contributing to cases in a meaningful way. The first step is to better understand the occurrence and range of associative value of these fingermarks. The project goal was to explore and test a theory that in large numbers of cases fingermarks of no value for identification purposes occur and are readily available, though not used, and yet have associative value that could provide useful information. Latent fingermarks were collected from nine state and local jurisdictions. Fingermarks included were those (1) collected in the course of investigations using existing jurisdictional procedures, (2) originally assessed by the laboratory as of no value for identification (NVID), (3) re-assessed by expert review as NVID, but with least three clear and reliable minutiae in relationship to one another, and (4) determined to show at least three auto-encoded minutiae. An expected associative value (ESLR) for each mark was measured, without reference to a putative source, based on modeling within-variability and between-variability of AFIS scores. This method incorporated (1) latest generation feature extraction, (2) a (minutiae-only) matcher, (3) a validated distortion model, and (4) NIST SD27 database calibration. Observed associative value distributions were determined for violent crimes, property crimes, and for existing objective measurements of latent print quality. 750 Non Identifiable Fingermarks (NIFMs) showed values of Log10 ESLR ranging from 1.05-10.88, with a mean value of 5.56 (s.d. 2.29), corresponding to an ESLR of approximately 380,000. It is clear that there are large numbers of cases where NIFMs occur that have high potential associative value as indicated by the ESLR. These NIFMs are readily available, but not used, yet have associative value that could provide useful information. These findings lead to the follow-on questions, \"How useful would NIFM evidence be in actual practice?\" and, \"What developments or improvements are needed to maximize this contribution?\"

The development of solid drug dosage form and food ingredients is constrained by their low solubility, low dissolution, low bioavailability and poor physicochemical properties. Formation of cocrystal is a novel and promising method to enhance and improve the properties of materials without breaking the covalent bonds.
The goal of this review is to summarize the cocrystals and their applications in the field of Active Pharmaceutical Ingredients (APIs) and food ingredients (AFIs), mainly on the effective improvements of APIs\' and AFIs\' pharmacokinetic, physicochemical and mechanical properties by the formation of cocrystals.
After years of research and development on cocrystals in the area of pharmaceutical and food industries, significant progress has been made. Formation of cocrystal is an efficient method for improving the solubility, dissolution rate, permeability and in vivo bioavailability of APIs and AFIs, as well as for enhancing stability and mechanical properties.
Cocrystals exhibit complex structures which can conspicuously affect the physical and chemical properties of original substance, with good clinical performance and outstanding stability during processing and storage.