Biometric Data Research Articles

Multimodal biometric integration: Trends and insights from the past quinquennial

Recent years have seen a notable breakthrough in multimodal biometric systems, which combine numerous biometric modalities for increased security and accuracy. This paper offers a thorough analysis of recent advances in the field, addressing the many multimodal biometric strategies that have been put out by different researchers. Numerous modalities are covered by the evaluation, such as palm print, speech, iris, facial features, palm print, fingerprint, body form, gait, and more. To obtain amazing results in terms of accuracy and security, researchers have used cutting-edge methods like convolutional neural networks (CNNs), support vector machines (SVM), recurrent neural networks (RNNs), deep learning, and optimization algorithms. Many fusion strategies have been investigated to efficiently merge data from many modalities, such as feature-level fusion, decision-level fusion, and score-level fusion. Developments in template protection systems have also addressed security issues related to transmission and storage of biometric data. Even though a lot of the suggested systems have shown great accuracy rates, there are still issues with hardware restrictions, dataset biases, privacy problems, and computational costs. Prospective study avenues encompass investigating more extensive and heterogeneous datasets, crafting resilient fusion algorithms, and amalgamating cutting-edge technologies like deep learning-based biometric cryptosystems and federated learning. All things considered, the literature study demonstrates the enormous potential of multimodal biometric systems in offering safe and dependable authentication solutions for a wide range of applications, from on-line student authentication and proctoring systems to customized healthcare networks.

A descriptive study of the biometrics of the stingless bee Lepidotrigona flavibasis in northern Thailand

Stingless bees play a crucial role in tropical and subtropical ecosystems, contributing significantly to biodiversity and pollination services. The species Lepidotrigona flavibasis (Cockerell 1929) (Apidae: Meliponini), found in Southeast Asia, has attracted attention for its unique nesting habits and valuable honey production. The objective of this study was to compare colony size and productivity between different nesting cavities. Twelve colonies of L. flavibasis were collected in Chiang Mai province, Thailand, to study biometrical data. Six colonies were housed in hollowed logs, previously occupied by Apis cerana, and six in bamboo cavities. The colony ages ranged from 8 months to 5 years. This study found that log cavity colonies had larger populations and more brood cells than bamboo cavity colonies. Results show that log cavity colonies had an average number of workers (3,842.6 ± 2036), drones (288.2 ± 511), brood cells (5,914.5 ± 3026), honey pots (290.5 ± 210), and pollen pots (96.8 ± 90). The bamboo cavity colonies were found to have an average number of workers (1,628.2 ± 575), drones (64 ± 58), brood cells (2,333.7 ± 1875), honey pots (127.3 ± 95), and pollen pots (94.8 ± 57). Overall, the log cavity colonies demonstrated greater colony size and productivity regarding adult bees and brood.

The Validity of Apple Watch Series 9 and Ultra 2 for Serial Measurements of Heart Rate Variability and Resting Heart Rate.

The widespread use of wearable devices has enabled continuous monitoring of biometric data, including heart rate variability (HRV) and resting heart rate (RHR). However, the validity of these measurements, particularly from consumer devices like Apple Watch, remains underexplored. This study aimed to validate HRV measurements obtained from Apple Watch Series 9 and Ultra 2 against the Polar H10 chest strap paired with the Kubios HRV software, which together served as the reference standard. A prospective cohort of 39 healthy adults provided 316 HRV measurements over a 14-day period. Generalized Estimating Equations were used to assess the difference in HRV between devices, accounting for repeated measures. Apple Watch tended to underestimate HRV by an average of 8.31 ms compared to the Polar H10 (p = 0.025), with a mean absolute percentage error (MAPE) of 28.88% and a mean absolute error (MAE) of 20.46 ms. The study found no significant impact of RHR discrepancies on HRV differences (p = 0.156), with RHR showing a mean difference of -0.08 bpm, an MAPE of 5.91%, and an MAE of 3.73 bpm. Equivalence testing indicated that the HRV measurements from Apple Watch did not fall within the pre-specified equivalence margin of ±10 ms. Despite accurate RHR measurements, these findings underscore the need for improved HRV algorithms in consumer wearables and caution in interpreting HRV data for clinical or performance monitoring.

Private Cctv Liability Under Biometric Data Protection Rules

The penetration of technology that is increasingly developing has a lot of impact on human life. Increasing technology also results in the rapid development of home security equipment, especially Closed-Circuit Television cameras (hereinafter mentioned as CCTV). The right to privacy, to which everyone is entitled to personal self- protection Doctrinal legal research is a crucial aspect in recognizing various societal problems. Doctrinal research combines a systematic analysis of the provisions of legislation and the legal principles contained in it or derived from it, but also the logical and rational order of legal propositions and principles. Doctrinal legal research methods involve the analysis of legal propositions or legal concepts on which the main study is based. Case base approach demands the researcher to understand the reasons that used by the judges or another adjudicator until it reaches the final decision. Most personal data protection laws around the world distinguish between regular personal data and biometric data. At the beginning, Article 4 paragraph (1) of EU GDPR regular personal data commonly define on the laws as a set of data that able to identify a natural person either from electronic or non-electronic devices. Under the data protection laws regime especially GDPR, UK Data Protection Law and Indonesia Data Protection Law, mitigation during a data protection failure involving household CCTV systems typically involves taking immediate steps to minimize the impact of the breach Furthermore, stated in the Fairhurst v Woodard case homeowner could be held liable for failure to protect the personal data if they use camera on the outside boundary of their house and they could face hefty fine and/or tools removal. Finally, this writing contributes to undergoing discourse on cyber law field especially in data protection law and provide insights for academician, practitioner, and society.

A Mathematical Modelling for Three-Factor Quantum Biometric Authentication Using Machine Learning, Double-Layer Encryption, Cryptographic Algorithms

Traditional authentication mechanisms are vulnerable to sophisticated attacks in the ever-changing cybersecurity paradigms. In this paper, a new three-factor quantum biometric system is proposed that were analysed to increase security level and reduce threats. This model employs multiple strategies, like quantum key distribution, a biometric identification system and machine learning which encrypts data based on their unique properties in an innovative way to provide not only one but two layers of encryption from unauthorized access. The system in its essence combines three critical elements: quantum generated cryptographic keys, biometric data (fingerprint or retinal scans) and dynamic behaviour-based identification using machine learning algorithms. By adding QKD to the hardware, an encryption key that should be practically impossible for anyone else (except sender and receiver) to intercept is securely generated and distributed through quantum mechanics principles of detection preventing possible eavesdropper. Secondly, the biometric data means that an imposter would have to not only steal your smartphone but also be able to authenticate themselves using unique physiology which takes even more of the likelihood out. Machine learning models study user behaviour patterns and work to adapt against evolving threats, gradually improving the accuracy of authentication with time. For added security, the system uses a double-layer encryption routine. The newly proposed system has a two-level encryption, and in the first layer, quantum keys encrypted bio-metric data while classical algorithms are applied to encrypt overall communication as well storage process. Besides ensuring the safety of private biometric information through dual encryption, this added redundancy also allows them to function as extra fail safe so that if one layer is breached, the entire system will still stand uncompromised. We evaluate the implementation of this new authentication scheme to a series-simulations and real-world test, showing the performance in different scenarios including high threat level environments. The results show major security enhancement compared to the traditional implementation of authentication methods with an observed amount decrease in data breaches and unsanctioned access attempts.

Performance Evaluation of a Simple Strategy to Optimize Formula Constants for Zero Mean or Minimal Standard Deviation or Root-Mean-Squared Prediction Error in Intraocular Lens Power Calculation

PurposeTo investigate the performance of a simple prediction scheme for the formula constants optimized for a mean (MPE), standard deviation (SDPE) or root-mean-squared refractive prediction error (RMSPE). DesignRetrospective cross-sectional study. MethodsUsing IOLMaster 700 biometric data from 888 eyes treated with the Hoya Vivinex lens and 821 eyes treated with the Alcon SA60AT lens, plus the power of the implanted lens and postoperative spherical equivalent refraction, optimized constants for SRKT, Hoffer Q, Holladay 1, Haigis, and K6 formulae were calculated using an iterative nonlinear optimization for zero MPE and minimal SDPE and RMSPE. Start values were detuned by ±1.5 from the MPE optimized constants and formula constants generated using the simple prediction scheme were compared to the corresponding directly optimized constants. ResultsFor all 5 formulae under test and with both datasets, constants optimized using the simple scheme showed excellent agreement with those from the iterative method with either MPE or RMSPE used as the optimization metric, and good agreement with SDPE as the metric. Constants optimized for zero MPE or minimal RMSPE agreed within 0.05, whereas constants for minimal SDPE could be systematically off by up to 0.6 from the MPE values, making SDPE unsuitable as an optimization metric. ConclusionsThis simple formula constant optimization scheme performs excellently for 4 disclosed formulae and one nondisclosed formula in our 2 monocentric datasets with zero MPE or minimal RMSPE as metrics. Multicentric studies with other study populations and biometers are required to further investigate the clinical applicability.

NOVEL SMART CONTACT LENSES WITH EMBEDDED AI FOR PIONEERING CONTINUOUS HEALTH TRACKING AND DISEASE DETECTION

Smart contact lenses equipped with embedded artificial intelligence (AI) represent a groundbreaking advancement in continuous health monitoring and disease detection. This paper explores the development and application of smart contact lenses that integrate real-time data collection with sophisticated AI algorithms to monitor various health parameters. By incorporating sensors and AI processing units directly into the lenses, these devices can continuously measure and analyze biometric data such as glucose levels, intraocular pressure, and ocular health indicators. The embedded AI system processes this data to provide real-time feedback, identify potential health issues, and predict disease onset with high accuracy. The study evaluates the efficacy of these smart contact lenses in detecting early signs of chronic conditions like diabetes and glaucoma, and their ability to enhance patient engagement through personalized health insights. Additionally, the paper discusses the technological challenges, including data accuracy, device comfort, and privacy concerns, and proposes solutions to address these issues. The integration of smart contact lenses with AI technology offers a novel approach to proactive health management, paving the way for more accessible and effective continuous monitoring and early disease detection.

Identifying PTSD sex-based patterns through explainable artificial intelligence in biometric data

Post-Traumatic Stress Disorder (PTSD) is a mental health condition that arises from exposure to traumatic events, affecting various aspects of human well-being. The complexity and variability of symptoms pose challenges for accurate diagnosis and monitoring, exacerbated by accessibility barriers. In response, alternative methodologies leveraging biometric data have emerged, such as facial movements, speech, or voice-to-text transcriptions, and analyzing them using Explainable Artificial Intelligence (XAI) techniques. Numerous studies have explored the presence or absence of PTSD, yet few have concentrated on either explicable indicators or distinctions among these indicators, such as the patient’s sex. This research used an XAI algorithm to identify patterns related to PTSD in three biometric data sets: facial movements, speech, and voice-to-text transcriptions. Such biometric data are grouped by sex. Utilizing the DAIC-WOZ database, this study involves feature selection and characterization. Experiment configurations addressed participant segmentation, feature reduction, and standardization. Training phases employed machine-learning classification algorithms with their corresponding performance evaluation. The interpretability stage explored the relationship between input features and class output. The findings reveal that among the three biometric data sets evaluated in this work (facial movements, speech, and voice-to-text transcriptions), speech characterization is the most effective in identifying PTSD indicators, suggesting a uniform speech pattern associated with breathy and tense voice and weak phonation in PTSD patients. Sex-specific analysis enhances prediction performance, revealing distinctions in the associated speech features. The Women’s model prioritizes tense voice and vocal volume variations, reduced glottal closure, and interrupted phonation. Conversely, the Speech-Men model reflects reduced resonance, making the voice thinner and weaker, indicating altered vocal quality. As for facial movements, sex-specific characteristics are not evident, but some features focused on lips are associated with PTSD. Similarly, PTSD is related to alertness, determination, and anxiety in both women and men. In conclusion, using an XAI algorithm to differentiate sex-based patterns in biometric data contributes to a better understanding of PTSD indicators, offering potential advancements in personalized diagnostic strategies.

Accuracy and role of consumer facing wearable technology for continuous monitoring during endoscopic procedures.

Consumer facing wearable devices capture significant amounts of biometric data. The primary aim of this study is to determine the accuracy of consumer-facing wearable technology for continuous monitoring compared to standard anesthesia monitoring during endoscopic procedures. Secondary aims were to assess patient and provider perceptions of these devices in clinical settings. Patients undergoing endoscopy with anesthesia support from June 2021 to June 2022 were provided a smartwatch (Apple Watch Series 7, Apple Inc., Cupertino, CA) and accessories including continuous ECG monitor and pulse oximeter (Qardio Inc., San Francisco, CA) for the duration of their procedure. Vital sign data from the wearable devices was compared to in-room anesthesia monitors. Concordance with anesthesia monitoring was assessed with interclass correlation coefficients (ICC). Surveys were then distributed to patients and clinicians to assess patient and provider preferences regarding the use of the wearable devices during procedures. 292 unique procedures were enrolled with a median anesthesia duration of 34 min (IQR 25-47). High fidelity readings were successfully recorded with wearable devices for heart rate in 279 (95.5%) cases, oxygen in 203 (69.5%), and respiratory rate in 154 (52.7%). ICCs for watch and accessories were 0.54 (95% CI 0.46-0.62) for tachycardia, 0.03 (95% CI 0-0.14) for bradycardia, and 0.33 (0.22-0.43) for oxygen desaturation. Patients generally felt the devices were more accurate (56.3% vs. 20.0% agree, p < 0.001) and more permissible (53.9% vs. 33.3% agree, p < 0.001) to wear during a procedure than providers. Smartwatches perform poorly for continuous data collection compared to gold standard anesthesia monitoring. Refinement in software development is required if these devices are to be used for continuous, intensive vital sign monitoring.

Reproductive biology of the commercial sea cucumber Holothuria (Roweothuria) poli, in the Central Aegean Sea, Greece

Holothuria poli is one of the most in-demand sea cucumber species and has attracted the interest of markets in Mediterranean regions. The present study is the first attempt to determine the reproductive cycle of H. poli in a coastal area of the Central Aegean Sea. Samples of H. poli as well as environmental and biometric data were collected monthly for 1 year. The sample population exhibited a female predominance, a size at first maturation of 156.3 mm and a distinctive reproductive pattern that reached its mature stage in the summer months. These findings contribute to the overall understanding of the reproductive physiology of the species and provide information for the sustainable management of sea cucumber stocks in the Hellenic Seas.

BIAS: A novel secure and efficient biometric-based anonymous authentication scheme

Currently, biometric-based authentication schemes are widely adopted in the field of online payments. Consequently, this has led to an increasing number of people becoming concerned about the privacy-preservation of their biometric data. Gunasinghe et al. presented PrivBioMTAuth, a solution for mobile phone biometric-based authentication designed to protect users’ privacy. However, the solution has drawbacks, such as its impact on the execution efficiency of the authentication protocol or its vulnerability to man-in-the-middle attacks during the authentication phase. Moreover, the user’s biometric image and the password must be revealed to the identity provider, which may raise security concerns. In this work, we present a novel secure and efficient biometric-based anonymous authentication solution with fully succinct verification and significantly lower storage and communication overhead. Different from PrivBioMTAuth, we rely on the NIZK argument given in Groth’s work to reduce the size of the anonymous identity and simplify the verification complexity. In addition, we design a high-performance protocol for conducting large-scale verification of the user’s anonymous identities. We propose an optimized multi-exponentiation argument based on Bayer et al.’s work and utilize it to ensure that a semi-trusted identity provider who seeks to access the users’ sensitive biometric information can faithfully execute the users’ identity registration protocol. The experiment results show that our proposed scheme is efficient and has privacy-preserving capabilities, and it can be applied in the resource-constrained devices.

Deep Feed-Forward Neural Network-Based Biometric Authentication System with Biometric Identity and Reputation Score in Blockchain

The biometric authentication in today’s digital era has become a vital issue in security and privacy. Biometrics is currently becoming more significant specifically for higher security modules. Here, a deep feed-forward neural network-based biometric authentication system (DFFNN_biometric authentication system) is presented for biometric authentication utilizing a biometric fingerprint image. In blockchain network, biometric data are considered and the fingerprint images are fed as input. As an input fingerprint image, minutiae extraction is accomplished. Thereafter, a deep key is generated employing a deep residual network (DRN) for template protection. Thereafter, privacy protection template generation is conducted and the templates thus obtained are stored on a database (DB). On the other hand, a query fingerprint image is given as input in the authentication stage. The processes such as minutiae extraction, deep key generation using DRN and privacy protection template authentication are carried out as like it performed in the blockchain network. Additionally, the matching process is conducted between the output acquired in the authentication phase and image stored in DB. The reputation score is generated from authenticated biometric behavior. The DFFNN is utilized to generate a reputation score. Moreover, the DFFNN_biometric authentication system obtained a maximum accuracy of 90.8%, maximum genuine accept rate (GAR) of 0.936, minimum false acceptance rate (FAR) of 0.588, minimum false rejection rate (FRR) of 0.556 and maximum reputation score of 9.346.

Banning biometric mind reading: the case for criminalising mind probing

ABSTRACT Several emerging technologies afford a specific type of interference with privacy which has not yet been conceptualised: inferring information about mental properties of persons on the basis of physiological or biometric signals, in short: mind probing. This paper conceptualises it, analyses several forms and proposes concrete norms that ban and criminalise it. Facial emotion recognition, neuroimaging, or wearable consumer devices such as smartwatches process biometric data to draw inferences about minds; the prevalence and sophistication of such mind probes will soon increase through AI data analysis. This paper explores how human rights law, the EU General Data Protection Regulation and the EU AI Act address mind probes and shows why the current level of protection appears insufficient. This motivates novel norms banning and criminalising non-consensual biometric-based forms of mind reading, ensuring that at least some parts of the mind remain in principle free from non-consensual mind probing.

Predicting Epileptic Seizures Using Biometric Sensors - A Pilot Study Protocol.

Epilepsy is a prevalent neurological disorder characterized by unpredictable seizures despite some known risk factors. Using an mHealth system and Empatica EmbracePlus, we aim to examine if biometric sensor data can detect and predict epileptic seizures. Five participants with epilepsy will be followed for a minimum of 15 seizures. Expected results and possible implications are presented. This study may provide new insights into biological indicators for seizure forecasting.

Biometric holographic encryption and authentication with multiple optics-biology keys based on inhomogeneous media optics

Biometric authentication has been widely used because of its uniqueness, accuracy, and versatility, but existing methods still have some shortcomings. Because biometric data is stored in the database, once the database is invaded, it will cause a large number of biometric data leakage and illegal use of huge risks; The cost of traditional feature extraction and matching strategy is too high; Biometric is only used for authentication, and its single function cannot meet the increasing security requirements; Methods that rely solely on biometric keys are not secure enough. To overcome these shortcomings, we propose the biometric holographic encryption and authentication with multiple optics-biology keys based on inhomogeneous media optics. The proposed method innovatively combines biometrics and inhomogeneous media optics to achieve dual functions of encryption and authentication. Instead of storing biometric data directly, the encryption and authentication information is encrypted as computer generated holograms in this method and stored in the database to reduce the risk of biometric data leakage. This method has low cost without complicated feature extraction and comparison authentication process. The dual functions of authentication and encryption is more practical. Multiple optics-biology keys improve the security. The security and robustness are analyzed. A graphical user interface (GUI) is designed for the method to promote its application. This work provides new methods and new ideas for the field of optics encryption and authentication, and will certainly have a broad application prospect.

Using Full Dive Virtual Reality to Operationalize Warfighter Resilience: From Proof of Concept and Usability of Hardware and Software to Upcoming Integrated Psychological Skills Training.

Modern warfare operations are volatile, highly complex environments, placing immense physiological, psychological, and cognitive demands on the warfighter. To maximize cognitive performance and warfighter resilience and readiness, training must address psychological stress to enhance performance. Resilience in the face of adversity is fundamentally rooted in an individual's psychophysiological stress response and optimized through decreased susceptibility to the negative impact of trauma exposure. The current project aims to optimize warfighter expertise, resilience, adaptability, and performance by utilizing a validated Full Dive Virtual Reality (FDVR) training platform to provide high-fidelity, safe, and scalable, controlled stress exposure in highly realistic simulated training scenarios with the most advanced, immersive technology available. Following Institutional Review Board approval and consent, 2 operators were fitted with high-fidelity virtual reality headsets with hand and eye tracking, full-body haptic feedback suits, a 360° omnidirectional treadmill, and Food and Drug Administration (FDA) cleared biometric monitors. Following acclimation, operators were placed in an industrial fire scenario and instructed to respond as a firefighter and paramedic, to search for and resuscitate any casualties, extinguish the fire, and exfiltrate safely. Following initial acclimation and after each demonstration (n = 2), 3 semistructured interviews asked operators their perceptions and experiences related to FDVR, focusing on usability, feasibility, and safety. Biometric data were continuously recorded using the Caretaker Medical VitalStream. Proof-of-concept (POC) testing proved that the FDVR training platform is usable, safe, and feasible. It creates an immersive environment with physiological responses to mimic realistic Mass Casualty Events (MCEs). Using a case study approach, transcript data were analyzed using thematic analysis. Three major themes emerged: Sensory deficits reduced realism, but sensory feedback improved fidelity, vestibular discord affected the virtual reality experience but only when the system did not respond naturally to operator movement after acclimation, and movement accommodations were made by operators to enhance usability, especially for fine motor movements. Biometric data analysis correlated timestamps from the VitalStream unit with operator responses to stress-inducing events (i.e., explosions, fires, and a deceased victim). Both operators exhibited significant physiological responses, including elevated heart rate, systolic blood pressure, and mean arterial pressure, particularly following explosions, encountering fire, and encountering the deceased victim within the training environment. The FDVR training platform overcomes the obstacles of in-person simulation training and provides the closest to real-life experience available. It will allow warfighters to train with their teams in immersive environments that replicate the conditions in which they are expected to perform their duties. The POC demonstrated that physiological responses can be mapped to scenario events to allow tracking of stress responses, cognitive load, as well as performance, and decision-making of the warfighter. The POC only involved 2 operators, but served to prove that the platform was safe and effective. Future testing plans to include 200 warfighters in operational teams of 10 to 12 to further validate the training effectiveness of the FDVR platform.

An Analysis of Ocular Biometrics: A Comprehensive Retrospective Study in a Large Cohort of Pediatric Cataract Patients.

Objectives: This study aims to provide a comprehensive analysis of ocular biometric parameters in pediatric patients with cataracts to optimize surgical outcomes. By evaluating various biometric data, we seek to enhance the decision-making process for intraocular lens (IOL) placement, particularly with advanced technologies like femtosecond lasers. Methods: This retrospective comparative study included pediatric patients with cataracts who underwent ocular biometric measurements and cataract extraction with anterior vitrectomy at the Medical University of Vienna between January 2019 and December 2021. Parameters measured included corneal diameter (CD), axial length (AL), corneal thickness (CT) and flat and steep keratometry (Kf and Ks). The study explored the correlations between these parameters and IOL placement. Results: A total of 136 eyes from 68 pediatric patients were included in the study. Significant positive correlations were found between corneal diameter, age and AL. The mean CD was 11.4 mm, mean AL was 19.5 mm, CT was 581.2 ± 51.8 µm, Kf was 7.76 ± 0.55 mm and Ks 7.41 ± 0.59 mm, respectively. Older pediatric patients with larger corneal diameters and longer ALs were more likely to receive in-the-bag IOL implantation. Conversely, younger patients often required alternative IOL placements or remained aphakic. Our data indicated that over 95% of the study population and all patients aged one year and older had a corneal diameter of 10 mm or larger. Conclusions: Detailed ocular biometric analysis is crucial for optimizing both surgical outcomes and postoperative care in pediatric cataract patients. The positive correlations between CD, age and AL underline the importance of individualized surgical planning tailored to each patient's unique anatomical features. Additionally, our findings suggest that the use of a femtosecond laser is both feasible and safe for pediatric patients aged one year and older, potentially offering enhanced surgical precision and improved outcomes.

Estimation of pupil size at iris plane and its magnification after cataract surgery.

To estimate the pupil size (at the iris plane) under photopic (PPH) and scotopic (PS) conditions after phacoemulsification with intraocular lens (IOL) implantation. This retrospective observational cohort study included 190 virgin eyes from 190 patients who underwent cataract surgery with IOL implantation. Data collected with Aladdin (Topcon), AS-OCT MS-39 (CSO), and iTrace (Tracey) were SimK, mean pupillary power at 6 mm (MPP), anterior chamber depth (ACD), lens thickness (LT), axial length (AL), lens rise (LR), PPH and PS before and after surgery at 30 days, dysfunctional lens index, and opacity grade. The position of the postoperative iris plane (PIP) was measured manually with MS-39, and a multivariate regression formula was developed to predict it. Statistical analysis was performed using Statistical Package for Social Science (SPSS) (IBM). The mean and standard deviations were 42.61 ± 3.20 D for MMP at 6 mm, 3.35 ± 0.37 mm for ACD, 3.89 ± 0.18 mm for PIP (P < 0.01), 4.55 ± 0.42 mm for LT, 0.43 ± 0.24 mm for LR, and 25.91 ± 3.03 mm for AL. The mean preoperative and postoperative topographic pupil magnification was 12% and 14.22%, respectively (P < 0.01). Despite an increase in magnification, the postoperative pupil was smaller than the preoperative one both for scotopic and photopic conditions: The larger the preoperative pupil, the more it tends to reduce in the postoperative period. Analysis of the preoperative topographic pupil alone is not sufficient for a correct indication of the optical zone and total diameter of IOL to be implanted but must be correlated with biometric data. The topographic pupil, therefore, undergoes a change in magnification from the preoperative period to the postoperative period. Furthermore, the real pupil presents a modification and, in most cases, tends to be smaller postoperatively in both photopic and scotopic conditions.

Generalised models of the vertebrate eye.

To present a set of closed-form analytical equations to create a consistent eye model balance based on clinically measured input parameters in a single step. These models complement the existing iterative approaches in the literature. Two different approaches are presented, both considering the cornea and lens as equivalent thin lenses. The first, called the Gaussian model, starts by defining the refractive error as the difference between the axial power (or dioptric distance) and the whole eye power, which can be expanded by filling in the formulas for each power. The resulting equation can be solved for either the refractive error, axial length, corneal power, lens power or the distance between the cornea and the lens as a function of the other four parameters. The second approach uses vergence calculations to provide alternative expressions, assuming that the refractive error is located at the corneal plane. Both models are explored for a biometric range typically found in adult human eyes. The Gaussian and vergence models each instantly balance the input data into a working eye model over the human physiological range and far beyond as demonstrated in various examples. The equations of the Gaussian model are more complicated, while the vergence model experiences more singularities, albeit in trivial or highly unlikely parameter combinations. The proposed equations form a flexible and robust platform to create eye models from clinical data. Possible applications lie in creating animal eye models or providing a generic reference for real biometric data and the relationships between the ocular dimensions.

Metacrime and Cybercrime: Exploring the Convergence and Divergence in Digital Criminality

The advent of the metaverse has given rise to metacrime, a novel category of criminal activities occurring in the metaverse, which not only challenges conventional digital criminality but existing law enforcement frameworks. To address the scholarship vacancy, this study examines the intersections and distinctions between metacrime and conventional cybercrime by employing a multi-disciplinary literature review and comparative analysis. We identified five shared characteristics between these two crime types: crime classification, continuous evolution, hyper-spatial-temporality (global reach), anonymity, and governance challenges. Crucially, our research highlights the distinct epistemological aspects of metacrime through its criminogenic, victimogenic, etiological, ethical, and regulatory dimensions, exemplified by virtual-to-physical attacks, immersive virtual reality attacks, victimization superrealism, complexities of human-avatar interactivity, excessive misuse of biometric data, increasingly vulnerable populations, and avatars’ liability. Our findings underscore the imperative need for tailored and forward-thinking regulatory responses to address the intricate challenges of metacrime, thereby ensuring the security and integrity of evolving digital environments.