Identification Procedures Research Articles

Subseasonal Ensemble Prediction of Flash Droughts over China

Abstract Flash droughts have been occurring frequently worldwide, which has a serious impact on food and water security. The rapid onset of flash droughts presents a challenge to the subseasonal forecast, but there is limited knowledge about their forecast skills due to the lack of appropriate identification and assessment procedures. Here, we investigate the forecast skill of flash droughts over China with lead times up to 3 weeks by using hindcast datasets from the Subseasonal-to-Seasonal Prediction (S2S) project. The flash droughts are identified by using weekly soil moisture percentiles from two S2S forecast models (ECMWF and NCEP). The comparison with reanalysis shows that ECMWF and NCEP forecast models underestimate flash drought occurrence by 5% and 19% for lead 1 week. The national mean hit rates for flash droughts are 0.22 and 0.16 for ECMWF and NCEP models for lead 1 week, and they can reach 0.29 and 0.18 over South China. The ensemble of the two models increases equitable threat score (ETS) from ECMWF and NCEP models by 8% and 40% for lead 1 week. In terms of probabilistic forecast, ECMWF has a higher Brier skill score than NCEP, especially over eastern China, which is consistent with higher temperature and precipitation forecast skill. The multimodel ensemble has the highest Brier skill score. This study suggests the importance of multimodel ensemble flash drought forecasting. Significance Statement Flash droughts have raised considerable concern, but whether they can be predicted at subseasonal time scales remains unclear. This study evaluates forecast skill of flash droughts over China based on ECMWF and NCEP hindcast data. Focusing on the historical flash drought events identified by the onset speed and duration, it is found that the ECMWF model outperformed the NCEP model with higher hit rates, lower false alarm ratios, and higher equitable threat scores, especially during the first week. However, less than 30% of the drought events can be captured in most regions by both models. An ensemble of the two models showed skill improvement against the ECMWF model for both deterministic and probabilistic forecasts.

CBRN Disaster Hospital Response and Preparedness. An Italian Civilian Military Cooperation Teaching Model

Introduction:Chemical, Biological, Radiological, and Nuclear (CBRN) emergencies need specific hospital preparedness and resource availability.Preparing to evaluate and manage victims from CBRN exposure events in one aspect of hospital preparedness, is often underestimated. Specific skills and capabilities are required to manage these events. Emergency department (ED) and hospital staff need adequate training to provide safe and effective care.Method:The Hospital Complex of Valtellina and Alto Lario (with three hospitals), in consideration of the geographic location amid mountains, far from urban hospital centers, decided to develop an intensive training program for the hospital emergency staff. Firstly, it was based on an eight-hour initial training program, using a combined civilian military approach that included hazard recognition, substance identification, site safety, response roles, PPE use, and decontamination procedures. The CBRN Operational Unit (for prevention of chemical-biological-radiological-nuclear risks) of the 1st Territorial Unit of the Auxiliary Military Corps of the Order of Malta Italy led such training session showing that a military approach to CBRN threats can be used with civilian and military competencies and tools in managing specific hazardous events hospital first responders may face.Results:A drill exercise was performed on a radiological fall-out incident to test the hospital’s emergency response staff preparedness. Hospital management and decontamination procedures were analyzed to treat victims as well as first responders and to train hospital staff with few resources available.Conclusion:Hospitals need a specific level of preparedness to enable an effective response to CBRN emergencies. Skills and competencies of military personnel can be a resource in these kinds of events to train civilian personnel who are not normally acquainted to or confident to manage this kind of CBRN events, and can represent a new model and challenge of interagency cooperation in the disaster management of complex emergencies involving hazardous materials.

Attenuated Total Reflection Fourier Transform Infrared Mapping for the Identification of the Prescribed and Abnormal Ingredients of Herbal Powder Preparations.

Herbal powder preparations (HPPs) are common forms of traditional medicine made by blending the powder of two or more ingredients. The first step to ensure the safety and efficacy of HPPs is to confirm the prescribed ingredients and screen the abnormal ingredients. With the help of attenuated total reflection Fourier transform infrared spectroscopy (ATR FT-IR) imaging or mapping, the particles of different ingredients in an HPP sample can be measured individually. In this way, the overlapped absorption signals of different ingredients in the ATR FT-IR spectrum of the bulk sample can be isolated in the ATR FT-IR spectra of the microscopic particles, which leads to the substantial increase of the specificity and sensitivity of the infrared spectral identification method. The characteristic particles of each ingredient can be identified by the objective comparison of the microscopic ATR FT-IR spectra against the reference spectra based on the correlation coefficients. Since the ATR FT-IR imaging or mapping tests of HPPs are free of the separation preprocess, multiple organic and inorganic ingredients are able to be recognized by a single identification procedure simultaneously rather than by different separation and identification procedures. As an example, the ATR FT-IR mapping method was used in this research to successfully identify three prescribed ingredients and two abnormal ingredients in oral ulcer pulvis, which is a classic HPP for oral ulcer in traditional Chinese medicine. The results show the feasibility of the ATR FT-IR microspectroscopic identification method for the objective and simultaneous identification of the prescribed and abnormal ingredients of HPPs.

Identification and validation of constitutive parameters of a Hencky-type discrete model via experiments on millimetric pantographic unit cells

Pantographic metamaterial design benefits from model identification procedures starting from what can be considered as the elementary unit cell of larger pantographic structures. Results from a tensile experiment and digital image correlation are utilized to identify the constitutive parameters of a discrete Hencky-type model for a millimetric pantographic cell. In the performed calibration, two different cost functions are formulated. First, the cost function is based upon measured resultant forces on the specimen boundaries. Then, the second cost function is based upon the measured pivot displacements in addition to reaction forces. The second cost functions thus exploits the pivot kinematics, which is a key feature of the deformation of pantographic structures. The identified model is further validated by predicting the reaction forces and pivot displacements of the same specimen subjected to compression. It is shown that the identification with the cost function incorporating pivot displacements is superior. It is also noted that the calibrated parameters deviate considerably from their initial guess derived from the linear Saint Venant problem, thereby indicating microscale nonlinear affects in otherwise linear reaction force-prescribed displacement responses at the macroscale.

Hybridization of Deep Learning Pre-Trained Models with Machine Learning Classifiers and Fuzzy Min-Max Neural Network for Cervical Cancer Diagnosis.

Medical image analysis and classification is an important application of computer vision wherein disease prediction based on an input image is provided to assist healthcare professionals. There are many deep learning architectures that accept the different medical image modalities and provide the decisions about the diagnosis of various cancers, including breast cancer, cervical cancer, etc. The Pap-smear test is the commonly used diagnostic procedure for early identification of cervical cancer, but it has a high rate of false-positive results due to human error. Therefore, computer-aided diagnostic systems based on deep learning need to be further researched to classify the pap-smear images accurately. A fuzzy min-max neural network is a neuro fuzzy architecture that has many advantages, such as training with a minimum number of passes, handling overlapping class classification, supporting online training and adaptation, etc. This paper has proposed a novel hybrid technique that combines the deep learning architectures with machine learning classifiers and fuzzy min-max neural network for feature extraction and Pap-smear image classification, respectively. The deep learning pretrained models used are Alexnet, ResNet-18, ResNet-50, and GoogleNet. Benchmark datasets used for the experimentation are Herlev and Sipakmed. The highest classification accuracy of 95.33% is obtained using Resnet-50 fine-tuned architecture followed by Alexnet on Sipakmed dataset. In addition to the improved accuracies, the proposed model has utilized the advantages of fuzzy min-max neural network classifiers mentioned in the literature.

Data Verification in the Agent, Combining Blockchain and Quantum Keys by Means of Multiple-Valued Logic

Network control of autonomous robotic devices involves a vast number of secured data coding, verification, and identification procedures to provide reliable work of distant agents. Blockchain scheme provides here the model of the extended linked list for the verification of critical data, approved by quasi-random hash values assigned by external network nodes. And quantum lines are the source of high-quality quasi-random keys used as hash values. Discrete multiple-valued logic in such procedures is a simple and flexible tool to form the logic linked list, combining critical internal parameters of agents with data taken from external nodes. Such combination enlarges the set of possible schemes for data protection from illegal modifications and for data restoration.

A complete method for assessing the effectiveness of eyewitness identification procedures: Expected information gain.

We present a method for measuring the efficacy of eyewitness identification procedures by applying fundamental principles of information theory. The resulting measure evaluates the expected information gain (EIG) for an identification attempt, a single value that summarizes an identification procedure's overall potential for reducing uncertainty about guilt or innocence across all possible witness responses. In a series of demonstrations, we show that EIG often disagrees with existing measures (e.g., diagnosticity ratios or area under the receiver operating characteristic) about the relative effectiveness of different identification procedures. Each demonstration is designed to highlight key distinctions between existing measures and EIG. An overarching theme is that EIG provides a complete measure of evidentiary value, in the sense that it factors in all aspects of identification performance. Collectively, these demonstrations show that EIG has substantial potential to inspire new discoveries in eyewitness research and provide a new perspective on policy recommendations for the use of identifications in real investigations. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Wireless standard-compliant e-health solution for elderly people with multiuser identification

ObjectiveOne of the main problems in e-health environments for developing an accurate multiuser identification is in the large number of patients, especially with itinerant medical devices and elderly people. This paper aims to contribute with two approaches to be included in ISO/IEEE 11073 family of standards as a standardized procedure for multiuser identification that can be used for a large variety of medical devices, regardless of their brand or model. From this contribution and to validate it, this work proposes a standardized e-health solution, including multiuser identification, implementing it in real health environments for elderly people, and evaluating their usability, interoperability, and adoption in daily living. Materials and methodsThis works implements the internationally recommended Personal Health Devices ISO/IEEE 11073 standards as a multiplatform environment (Windows/Linux/Mac OS) and fulfils the paradigms of scalability, modularity, portability, maintainability, and robustness. The standard e-health solution has been implemented in 26 health environments in several cities in Spain (Madrid, Barcelona, Sevilla, Zaragoza, etc.), consisting of 118 health professionals, 319 senior patients, and 18 technical professionals. ResultsThe proposed multiuser identification reduces the human error rate (from 13.3% to less than 5%) with positive evaluation: almost 70% of users are satisfied, with usability and time savings that are more than 50% in all the groups (nursing, medicine, and caregiving) and environments (residences, health centres, and hospitals). DiscussionThe use of e-health solutions within multiuser identification, as it is proposed through two standard-compliant approaches, permits advanced services and data analysis for a large variety of medical devices, regardless of their brand or model. ConclusionThis paper contributes an open interoperable e-health solution as alternative to the closed and commercial solutions and enables third-party developers to work collaboratively and extend the already implemented features, owing to the design based on plugins, value-added services, and multiple transport technologies and protocols.

Laser spot thermography and Pulse thermography – comparison of performance for non-destructive testing of composite structures

Laser spot thermography (LST) is a sub-domain method in active thermography that uses a laser as the heat source. The method allows for precise control of the beam shape, pulse duration, and delivered energy. Therefore, this technique can be applied, especially for delicate structures where testing conditions must be maintained precisely. The following work includes the introduction of a laser spot thermography technology demonstrator that was designed and constructed at AGH UST. The paper presents laser thermography laboratory experiments along with the developed procedure for automatic defect identification. The measurements were used to establish an automatic procedure and algorithm for automatic defect identification based on regression methods.

Evacuation of Refugees from Afghanistan: Health Control at the Spanish Border in the Context of the COVID-19 Pandemic

Following the announcement of the retreat of troops from Afghanistan, the Spanish Government organised the so-called "Antigone Operation" for the evacuation of Afghan collaborators. The most relevant ministries were involved in the response. The Ministry of Health, through the Foreign Health Department, performed the health control on arrival. The whole operation was conducted at an air base. It included the health control of refugees composed of temperature measurement, a basic visual control and a coronavirus disease (COVID-19) rapid antigen test for those over 12 years of age; the assessment of their basic needs (food and hygiene); identification and security procedures; and the initial administrative processing. The refugees were accommodated in a temporary facility at the base, where they waited to be transferred to their final destinations. Between 19 and 27 August 2021, 2168 refugees arrived on 17 flights; 680 of them were children under 12 years of age. One thousand four hundred and ninety-nine rapid antigen tests were performed, with one positive result. "Antigone Operation" is unprecedented in Spain and is one of the most complex operations carried out in recent years. The COVID-19 pandemic required the establishment of a health control system on arrival, performed by Foreign Health, which contributed significantly to the overall success of the operation.

Application of polyphasic approach for identification of epiphytic fungi isolated from wheat grains

In this study, epiphytic fungi were isolated from wheat seeds (Triticum aestivum L) and a polyphasic approach for their identification was applied. The initial morphological characterisation was done on selected twenty-tree strains and continued with their identification based on the carbon utilisation pattern according to the Biolog OmniLog system protocol (Hayward, CA, USA). Throughout the isolated strains, the Biolog data indicated predominance of the genera Aspergillus, Penicillium and Fusarium. The carbon utilisation pattern of five strains assigned them to several different from aforementioned genera and due to their scarcity they were not included in the current study. The indicated by the Biolog genus designation of the strains was used as a guideline for the further molecular identification of seventeen strains from the predominant genera. Molecular identification was based on a polymerase chain reaction (PCR) with universal primers, which targeted the internal transcribed spacer (ITS) region of rDNA gene. The PCR fragments were sequenced and after applying a BLAST algorithm, a high percentage of similarity was found for eleven strains. At the species level, the Biolog and molecular technique showed a discrepancy in the identification of two strains. The procedure of identification was unsuccessful for six strains either due to insufficient quantity of the PCR product or the lack of sufficient similarity of the fragments to the GenBank database. Current study showed that the unbiased identification of epiphytic fungi requires a polyphasic approach, which applies morphological, physiological and molecular techniques.

Adaptation and Utilization Phase of Radio Frequency Identification (RFID) Technology at Lancang Kuning University

The development of computer technology that is in line with the human will to facilitate daily work, this gave rise to the idea of being able to develop inventions to be used both individually and in groups as part of an agency/institutional organization. In recent years, automatic identification (Auto-ID) procedures have become very popular especially in campus and industrial environments; contactless identification has developed into an independent interdisciplinary field, which is no longer suitable for the use of any (conventional) single-discipline focus. The RFID technology in this case is a discipline of several elements from very diverse disciplines: databases, RF and EMC technology, semiconductor technology, data protection and cryptography, telecommunications, manufacturing technology, and many related fields. The linkage of this research with developed technology (RFID) are several stages of analyzing the readiness of Lancang Kuning University in adopting the latest technological developments, based on a priority scale; analysis of input from the survey followed by making analysis of project development in the form of modeling and equipment requirements to build RFID technology according to priority scale. The method used follows the rules and stages of structured research in the form of experimental research methods, review of urgency background, survey techniques, analysis and prototype design produced in the form of recommendations. It is hoped that this report can be used as a reference in the development and stages that can be useful for further development and research.

Mind the gap: Bridging evidence-based witness identification procedures to practice through police training

Research has led to evidence-based eyewitness identification procedures, but empirical research on how to train police officers in such techniques is limited. We tested the effectiveness of the FAIR (Find a suspect, Avoiding bias, Instructing the witness, Record the procedure) eyewitness identification training program with 88 Brazilian police officers. The hypothesis that FAIR training would improve identification procedures was supported by pre- and post-training assessment of performance on a lineup construction task. Training increased the likelihood that police officers would construct lineups with known-innocent fillers and provide recommended pre-lineup instructions to witnesses (e.g., stating that the witness is not required to make an identification). Training also decreased non-recommended lineup practices, such as revealing the identity of the main suspect after a witness response. Feedback from the participants supports the conclusion that FAIR training improved knowledge of how to build lineups, instruct witnesses, and avoid undesirable and potentially biasing practices. Nevertheless, participants anticipated that incorporating the reform procedures into practice would provoke resistance from superiors and colleagues, which highlights the need for FAIR training to be supported by infrastructure, resources and policy that enable police officers to use evidence-based procedures in eyewitness identification.

Validation of a new viscoelastic model for unidirectional polymer matrix composites by analytical and numerical homogenisation

The long-term response of unidirectional, fibres-reinforced, polymer matrix composites is affected by the viscoelastic behaviour of the matrix. Most composite models introduce a different relaxation function for each of the five elastic parameters describing the transversely isotropic response at the scale of the homogenised ply, thus requiring long identification procedures based on multiple experimental tests. A new transversely isotropic model in the small strains regime was recently introduced by the authors, who used a Cartan decomposition in order to derive a new set of elastic parameters, which are more directly related to the behaviour of the underlying constituents. In particular, the deviatoric viscoelastic response of the matrix strongly affects only two of the five newly introduced parameters. In this work, analytical and numerical homogenisation approaches are developed and implemented in order to challenge such hypothesis and to validate the proposed model. In particular, a periodic homogenisation strategy enabling to impose general loading paths in terms of macroscopic strains and/or stresses on a Representative Volume Element (RVE) was developed in Abaqus, and used as a “virtual testing machine” to provide data to compare the transversely isotropic composite model proposed by the authors to the homogenised response of the RVE.

GPMeta: a GPU-accelerated method for ultrarapid pathogen identification from metagenomic sequences.

Metagenomic sequencing (mNGS) is a powerful diagnostic tool to detect causative pathogens in clinical microbiological testing owing to its unbiasedness and substantially reduced costs. Rapid and accurate classification of metagenomic sequences is a critical procedure for pathogen identification in dry-lab step of mNGS test. However, clinical practices of the testing technology are hampered by the challenge of classifying sequences within a clinically relevant timeframe. Here, we present GPMeta, a novel GPU-accelerated approach to ultrarapid pathogen identification from complex mNGS data, allowing users to bypass this limitation. Using mock microbial community datasets and public real metagenomic sequencing datasets from clinical samples, we show that GPMeta has not only higher accuracy but also significantly higher speed than existing state-of-the-art tools such as Bowtie2, Bwa, Kraken2 and Centrifuge. Furthermore, GPMeta offers GPMetaC clustering algorithm, a statistical model for clustering and rescoring ambiguous alignments to improve the discrimination of highly homologous sequences from microbial genomes with average nucleotide identity >95%. GPMetaC exhibits higher precision and recall rate than others. GPMeta underlines its key role in the development of the mNGS test in infectious diseases that require rapid turnaround times. Further study will discern how to best and easily integrate GPMeta into routine clinical practices. GPMeta is freely accessible to non-commercial users at https://github.com/Bgi-LUSH/GPMeta.

A primer on pollen assignment by nanopore-based DNA sequencing

The possibility to identify plants based on the taxonomic information coming from their pollen grains offers many applications within various biological disciplines. In the past and depending on the application or research in question, pollen origin was analyzed by microscopy, usually preceded by chemical treatment methods. This procedure for identification of pollen grains is both time-consuming and requires expert knowledge of morphological features. Additionally, these microscopically recognizable features usually have a low resolution at species-level. Since a few decades, DNA has been used for the identification of pollen taxa, as sequencing technologies evolved both in their handling and affordability. We discuss advantages and challenges of pollen DNA analyses compared to traditional methods. With readers with little experience in this field in mind, we present a hands-on primer for genetic pollen analysis by nanopore sequencing. As our lab mainly works with pollen collected within agroecological research projects, we focus on pollen collected by pollinating insects. We briefly consider sample collection, storage and processing in the laboratory as well as bioinformatic aspects. Currently, pollen metabarcoding is mostly conducted with next-generation sequencing methods that generate short sequence reads (<1 kb). Increasingly, however, pollen DNA analysis is carried out using the long-read generating (several kb), low-budget and mobile MinION nanopore sequencing platform by Oxford Nanopore Technologies. Therefore, we are focusing on aspects for palynology with the MinION DNA sequencing device.

Identification for the cortical 3-Hinges folding pattern based on cortical morphological and structural features.

The Cortical 3-Hinges Folding Pattern (i.e., 3-Hinges) is one of the brain's hallmarks, and it is of great reference for predicting human intelligence, diagnosing eurological diseases and understanding the brain functional structure differences among gender. Given the significant morphological variability among individuals, it is challenging to identify 3-Hinges, but current 3-Hinges researches are mainly based on the computationally expensive Gyral-net method. To address this challenge, this paper aims to develop a deep network model to realize the fast identification of 3-Hinges based on cortical morphological and structural features. The main work includes: (1) The morphological and structural features of the cerebral cortex are extracted to relieve the imbalance between the number of 3-Hinges and each brain image's voxels; (2) The feature vector is constructed with the K nearest neighbor algorithm from the extracted scattered features of the morphological and structural features to alleviate over-fitting in training; (3) The squeeze excitation module combined with the deep U-shaped network structure is used to learn the correlation of the channels among the feature vectors; (4) The functional structure roles that 3-Hinges plays between adolescent males and females are discussed in this work. The experimental results on both adolescent and adult MRI datasets show that the proposed model achieves better performance in terms of time consumption. Moreover, this paper reveals that cortical sulcus information plays a critical role in the procedure of identification, and the cortical thickness, cortical surface area, and volume characteristics can supplement valuable information for 3-Hinges identification to some extent. Furthermore, there are significant structural differences on 3-Hinges among adolescent gender.

Grey-box model and neural network disturbance predictor identification for economic MPC in building energy systems

This paper presents a novel model identification procedure to treat the occupancy induced unmeasured heat disturbance in the building model identification step for a model predictive control (MPC) implementation. First, we present a grey-box identification framework to estimate a dynamic building model that systematically accounts for the large unmeasured disturbance in the training data. Then, we discuss an approach to compute the confidence intervals (CIs) on the parameters estimated in the building model. Next, we develop a framework to identify a neural network (NN) using historical operational data to give the feedforward predictions of the heat disturbance in the MPC optimization problem. Further, we discuss the application of the composite building model and the NN disturbance predictor for energy cost optimization using an economic MPC formulation. We present case studies using a simple two time scale based building model, and demonstrate the effectiveness of the proposed grey-box model and the NN disturbance predictor identification procedures. We perform closed-loop simulations to highlight that additional energy cost savings of up to 4–5% are provided by including the feedforward NN disturbance predictions in the MPC controller. Finally, we also demonstrate the suitability of the proposed two-step model identification procedure to estimate accurate and reliable models using an industrial data set collected from a real office building.

A Three-Step Screening Procedure for Early Identification of Children at High Risk of Hemophagocytic Lymphohistiocytosis.

The first step in diagnosing hemophagocytic lymphohistiocytosis (HLH) is to suspect its presence and then order the appropriate diagnostic tests. The development of screening procedures for HLH could facilitate early diagnosis. In this study, we evaluated the utility of fever, splenomegaly, and cytopenias as screening criteria for identifying pediatric HLH at an early stage, built a screening model using commonly measured laboratory parameters, and developed a step-wise screening procedure for pediatric HLH. The medical records of 83,965 pediatric inpatients, including 160 patients with HLH, were collected retrospectively. The utility of fever, splenomegaly, hemoglobin level, and platelet and neutrophil counts at hospital admission as screening criteria for HLH was evaluated. For HLH patients who might be missed by screening based on the presence of fever, splenomegaly, and cytopenias, a screening model using common laboratory parameters was developed. Following that, a three-step screening procedure was then developed. The criteria of cytopenias affecting two or more lineages plus fever or splenomegaly had a sensitivity of 51.9% and a specificity of 98.4% for identifying HLH in pediatric inpatients. Our screening score model comprises six parameters: splenomegaly, platelet count, neutrophil count, albumin level, total bile acid level, and lactate dehydrogenase level. The use of the validation set had a sensitivity of 87.0% and a specificity of 90.6%. A three-step screening procedure has been developed: Step 1: Is fever or splenomegaly present? (Yes: risk for HLH should be considered, go to Step 2; No: less likely HLH); Step 2: Are cytopenias affecting at least two lineages? (Yes: consider HLH; No: go to Step 3); Step 3: Calculate the screening score. Is the sum of the score greater than 37? (Yes: consider HLH; No: less likely HLH). The overall sensitivity and specificity of the three-step screening procedure were 91.9% and 94.4%, respectively. A significant proportion of pediatric HLH patients present at the hospital without having all three symptoms: fever, splenomegaly, and cytopenias. Our three-step screening procedure, utilizing commonly available clinical and laboratory parameters, can effectively identify pediatric patients who may be at high risk for HLH.

Lichenometric Dating and Its Limitations and Problems: A Guide for Practitioners

Lichenometry, a method for dating rock surface exposure mainly in high latitudes and mountain environments, is based on estimates of lichen growth rates, but over the last 70 years it has been severely criticised. Its chief limitation is its questionable reliability due to three main problems: the species belonging to the Rhizocarpon subgenus, most often used by lichenometrists, are hard to identify; growth studies have highlighted the intrinsic variability of growth both seasonally and annually, with species sensitively responding to a wide range of environmental factors; and the same sensitive dependence also applies to the time taken for colonization on fresh rock surfaces. These problems cast doubt on many absolute dating studies and critics have suggested that, at best, the technique should only be used for relative dating. This paper provides guidance on identification procedures and suggests alternative dating methods based on lichen size-frequency distributions and cross-dating with other lichen species, thus avoiding reliance on a single species or support from other methods. With appropriate development, it is hoped that these approaches can provide a way forward that allows the technique to contribute more reliably to the dating of rock surfaces in regions where there are few other dating options.