The Effect of Stress on a Personal Identification System Based on Electroencephalographic Signals.

Personal identification systems based on electroencephalographic (EEG) signals have their own strengths and limitations. The stability of EEG signals strongly affects such systems. The human emotional state is one of the important factors that affects EEG signals' stability. Stress is a major emotional state that affects individuals' capability to perform day-to-day tasks. The main objective of this work is to study the effect of mental and emotional stress on such systems. Two experiments have been performed. In the first, we used hand-crafted features (time domain, frequency domain, and non-linear features), followed by a machine learning classifier. In the second, raw EEG signals were used as an input for the deep learning approaches. Different types of mental and emotional stress have been examined using two datasets, SAM 40 and DEAP. The proposed experiments proved that performing enrollment in a relaxed or calm state and identification in a stressed state have a negative effect on the identification system's performance. The best achieved accuracy for the DEAP dataset was 99.67% in the calm state and 96.67% in the stressed state. For the SAM 40 dataset, the best accuracy was 99.67%, 93.33%, 92.5%, and 91.67% for the relaxed state and stress caused by identifying mirror images, the Stroop color-word test, and solving arithmetic operations, respectively.

From Traditional to Intelligent, A Review of Application and Progress of Sensory Analysis in Alcoholic Beverage Industry.

Sensory analysis is an interdisciplinary field that combines multiple disciplines to analyze food qualitatively and quantitatively. At present, this analysis method has been widely used in product development, quality control, marketing, flavor analysis, safety supervision and inspection of alcoholic beverages. Due to the changing needs of analysis, new and more optimized methods are still emerging. Thereinto, intelligent and biometric technologies with growing attention have also been applied to sensory analysis. This work summarized the sensory analysis methods from three aspects, including traditional artificial sensory analysis, intelligent sensory technology, and innovative technologies. Meanwhile, the application sensory analysis in alcoholic beverages and its industrial production was scientifically emphasized. Moreover, the future tendency of sensory analysis in the alcoholic beverage industry is also highlights.

Enhancing gait recognition by multimodal fusion of mobilenetv1 and xception features via PCA for OaA-SVM classification.

Gait recognition has become an increasingly promising area of research in the search for noninvasive and effective methods of person identification. Its potential applications in security systems and medical diagnosis make it an exciting field with wide-ranging implications. However, precisely recognizing and assessing gait patterns is difficult, particularly in changing situations or from multiple perspectives. In this study, we utilized the widely used CASIA-B dataset to observe the performance of our proposed gait recognition model, with the aim of addressing some of the existing limitations in this field. Fifty individuals are randomly selected from the dataset, and the resulting data are split evenly for training and testing purposes. We begin by excerpting features from gait photos using two well-known deep learning networks, MobileNetV1 and Xception. We then combined these features and reduced their dimensionality via principal component analysis (PCA) to improve the model's performance. We subsequently assessed the model using two distinct classifiers: a random forest and a one against all support vector machine (OaA-SVM). The findings indicate that the OaA-SVM classifier manifests superior performance compared to the others, with a mean accuracy of 98.77% over eleven different viewing angles. This study is conducive to the development of effective gait recognition algorithms that can be applied to heighten people's security and promote their well-being.

Outlier detection for keystroke biometric user authentication.

User authentication is a fundamental aspect of information security, requiring robust measures against identity fraud and data breaches. In the domain of keystroke dynamics research, a significant challenge lies in the reliance on imposter datasets, particularly evident in real-world scenarios where obtaining authentic imposter data is exceedingly difficult. This article presents a novel approach to keystroke dynamics-based authentication, utilizing unsupervised outlier detection techniques, notably exemplified by the histogram-based outlier score (HBOS), eliminating the necessity for imposter samples. A comprehensive evaluation, comparing HBOS with 15 alternative outlier detection methods, highlights its superior performance. This departure from traditional dependence on imposter datasets signifies a substantial advancement in keystroke dynamics research. Key innovations include the introduction of an alternative outlier detection paradigm with HBOS, increased practical applicability by reducing reliance on extensive imposter data, resolution of real-world challenges in simulating fraudulent keystrokes, and addressing critical gaps in existing authentication methodologies. Rigorous testing on Carnegie Mellon University's (CMU) keystroke biometrics dataset validates the effectiveness of the proposed approach, yielding an impressive equal error rate (EER) of 5.97%, a notable area under the ROC curve of 97.79%, and a robust accuracy (ACC) of 89.23%. This article represents a significant advancement in keystroke dynamics-based authentication, offering a reliable and efficient solution characterized by substantial improvements in accuracy and practical applicability.

Fractional-order Sprott K chaotic system and its application to biometric iris image encryption.

Fractional-order (FO) chaotic systems exhibit random sequences of significantly greater complexity when compared to integer-order systems. This feature makes FO chaotic systems more secure against various attacks in image cryptosystems. In this study, the dynamical characteristics of the FO Sprott K chaotic system are thoroughly investigated by phase planes, bifurcation diagrams, and Lyapunov exponential spectrums to be utilized in biometric iris image encryption. It is proven with the numerical studies the Sprott K system demonstrates chaotic behaviour when the order of the system is selected as 0.9. Afterward, the introduced FO Sprott K chaotic system-based biometric iris image encryption design is carried out in the study. According to the results of the statistical and attack analyses of the encryption design, the secure transmission of biometric iris images is successful using the proposed encryption design. Thus, the FO Sprott K chaotic system can be employed effectively in chaos-based encryption applications.

Rationale, Design, and Baseline Characteristics of Participants in the Health@NUS mHealth Augmented Cohort Study Examining Student-to-Work Life Transition: Protocol for a Prospective Cohort Study.

BACKGROUND: Integration of mobile health data collection methods into cohort studies enables the collection of intensive longitudinal information, which gives deeper insights into individuals' health and lifestyle behavioral patterns over time, as compared to traditional cohort methods with less frequent data collection. These findings can then fill the gaps that remain in understanding how various lifestyle behaviors interact as students graduate from university and seek employment (student-to-work life transition), where the inability to adapt quickly to a changing environment greatly affects the mental well-being of young adults. OBJECTIVE: This paper aims to provide an overview of the study methodology and baseline characteristics of participants in Health@NUS, a longitudinal study leveraging mobile health to examine the trajectories of health behaviors, physical health, and well-being, and their diverse determinants, for young adults during the student-to-work life transition. METHODS: University students were recruited between August 2020 and June 2022 in Singapore. Participants would complete biometric assessments and questionnaires at 3 time points (baseline, 12-, and 24-month follow-up visits) and use a Fitbit smartwatch and smartphone app to continuously collect physical activity, sedentary behavior, sleep, and dietary data over the 2 years. Additionally, up to 12 two-week-long bursts of app-based ecological momentary surveys capturing lifestyle behaviors and well-being would be sent out among the 3 time points. RESULTS: Interested participants (n=1556) were screened for eligibility, and 776 participants were enrolled in the study between August 2020 and June 2022. Participants were mostly female (441/776, 56.8%), of Chinese ethnicity (741/776, 92%), undergraduate students (759/776, 97.8%), and had a mean BMI of 21.9 (SD 3.3) kg/m2, and a mean age of 22.7 (SD 1.7) years. A substantial proportion were overweight (202/776, 26.1%) or obese (42/776, 5.4%), had indicated poor mental well-being (World Health Organization-5 Well-Being Index ≤50; 291/776, 37.7%), or were at higher risk for psychological distress (Kessler Psychological Distress Scale ≥13; 109/776, 14.1%). CONCLUSIONS: The findings from this study will provide detailed insights into the determinants and trajectories of health behaviors, health, and well-being during the student-to-work life transition experienced by young adults. TRIAL REGISTRATION: ClinicalTrials.gov NCT05154227; https://clinicaltrials.gov/study/NCT05154227. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/56749.

Two-Layered Multi-Factor Authentication Using Decentralized Blockchain in an IoT Environment.

Internet of Things (IoT) technology is evolving over the peak of smart infrastructure with the participation of IoT devices in a wide range of applications. Traditional IoT authentication methods are vulnerable to threats due to wireless data transmission. However, IoT devices are resource- and energy-constrained, so building lightweight security that provides stronger authentication is essential. This paper proposes a novel, two-layered multi-factor authentication (2L-MFA) framework using blockchain to enhance IoT devices and user security. The first level of authentication is for IoT devices, one that considers secret keys, geographical location, and physically unclonable function (PUF). Proof-of-authentication (PoAh) and elliptic curve Diffie-Hellman are followed for lightweight and low latency support. Second-level authentication for IoT users, which are sub-categorized into four levels, each defined by specific factors such as identity, password, and biometrics. The first level involves a matrix-based password; the second level utilizes the elliptic curve digital signature algorithm (ECDSA); and levels 3 and 4 are secured with iris and finger vein, providing comprehensive and robust authentication. We deployed fuzzy logic to validate the authentication and make the system more robust. The 2L-MFA model significantly improves performance, reducing registration, login, and authentication times by up to 25%, 50%, and 25%, respectively, facilitating quicker cloud access post-authentication and enhancing overall efficiency.

Facial recognition technology: regulations, rights and the rule of law.

Despite their pronounced potential, unacceptable risk AI systems, such as facial recognition, have been used as tools for, inter alia, digital surveillance, and policing. This usage raises concerns in relation to the protection of basic freedoms and liberties and upholding the rule of law. This article contributes to the legal discussion by investigating how the law must intervene, control, and regulate the use of unacceptable risk AI systems that concern biometric data from a human-rights and rule of law perspective. In doing so, the article first examines the collection of biometric data and the use of facial recognition technology. Second, it describes the nature of the obligation or duty of states to regulate in relation to new technologies. The article, lastly, assesses the legal implications resulting from the failure of states to regulate new technologies and investigates possible legal remedies. The article uses some relevant EU regulations as an illustrative example.

Ocular biometric characteristics of Han ethnicity in Tianjin and Uyghur ethnicity in Xinjiang undergoing cataract surgery.

AIM: To analyze and compare the differences among ocular biometric parameters in Han and Uyghur populations undergoing cataract surgery. METHODS: In this hospital-based prospective study, 410 patients undergoing cataract surgery (226 Han patients in Tianjin and 184 Uyghur patients in Xinjiang) were enrolled. The differences in axial length (AL), anterior chamber depth (ACD), keratometry [steep K (Ks) and flat K (Kf)], and corneal astigmatism (CA) measured using IOL Master 700 were compared between Han and Uyghur patients. RESULTS: The average age of Han patients was higher than that of Uyghur patients (70.22±8.54 vs 63.04±9.56y, P<0.001). After adjusting for age factors, Han patients had longer AL (23.51±1.05 vs 22.86±0.92 mm, P<0.001), deeper ACD (3.06±0.44 vs 2.97±0.37 mm, P=0.001), greater Kf (43.95±1.40 vs 43.42±1.69 D, P=0.001), steeper Ks (45.00±1.47 vs 44.26±1.71 D, P=0.001), and higher CA (1.04±0.68 vs 0.79±0.65, P=0.025) than Uyghur patients. Intra-ethnic male patients had longer AL, deeper ACD, and lower keratometry than female patients; however, CA between the sexes was almost similar. In the correlation analysis, we observed a positive correlation between AL and ACD in patients of both ethnicities (rHan =0.48, rUyghur =0.44, P<0.001), while AL was negatively correlated with Kf (rHan =-0.42, rUyghur =-0.64, P<0.001) and Ks (rHan =-0.38, rUyghur =-0.66, P<0.001). Additionally, Kf was positively correlated with Ks (rHan =0.89, rUyghur =0.93, P<0.001). CONCLUSION: There are differences in ocular biometric parameters between individuals of Han ethnicity in Tianjin and those of Uyghur ethnicity in Xinjiang undergoing cataract surgery. These ethnic variances can enhance our understanding of ocular diseases related to these parameters and provide guidance for surgical procedures.

Biometric factors associated with the postoperative visual performance of a multifocal intraocular lens.

Purpose: To identify the biometric factors associated with postoperative visual performance after uneventful phacoemulsification with multifocal intraocular lens (MIOL) implantation. Methods: In this retrospective cohort study, 72 eyes of 72 patients implanted with the HumanOptics Diff-aAY MIOL were included. Preoperative examination data including the white-to-white distance (WTW), anterior chamber depth (ACD), axial length and corneal astigmatism were gathered through the electronic medical records. One month postoperatively, the pupil parameters, corneal aberrations, corneal astigmatism, IOL tilts and IOL decentrations were measured using an OPD-Scan III aberrometer. Postoperative visual performance parameters were recorded as the visual acuity, depth of focus, modulation transfer function (MTF) and point spread function (PSF) values, area under log contrast sensitivity function (AULCSF), retinal straylight and visual function questionnaire scores. Univariate and multivariate linear regression analyses were then performed to evaluate the associations between the potential biometric factors and postoperative visual outcomes. Results: Younger age predicted greater MTF and PSF values, better AULCSF and better retinal straylight (P < 0.05). A lower corneal trefoil predicted better MTF and PSF values (P < 0.05). Smaller IOL decentration predicted better distance-corrected near visual acuity, greater AULCSF and better retinal straylight (P < 0.05). A less negative spherical equivalent (SE) predicted better MTF values (P = 0.017), while a more negative SE predicted better Visual Function Index-14 (VF-14) questionnaire scores and satisfaction scores (P < 0.05). A higher IOL power predicted better best corrected distance visual acuity (P = 0.005). Lower preoperative corneal astigmatism predicted greater MTF values (P = 0.020). Lower postoperative corneal astigmatism, smaller corneal high-order aberrations (HOAs), smaller photopic pupil size, larger WTW and deeper ACD predicted a better AULCSF (P < 0.05). Conclusions: IOL decentration, IOL power, age, preoperative and postoperative corneal astigmatism, SE, photopic pupil size, corneal trefoil, WTW, ACD and corneal HOAs were significantly associated with postoperative visual performance. These findings might aid in patient selection prior to MIOL implantation.

A multi-factor knuckle and nail bed verification tool for forensic imagery analysis.

BACKGROUND: The grooming process involves sexually explicit images or videos sent by the offender to the minor. Although offenders may try to conceal their identity, these sexts often include hand, knuckle, and nail bed imagery. OBJECTIVE: We present a novel biometric hand verification tool designed to identify online child sexual exploitation offenders from images or videos based on biometric/forensic features extracted from hand regions. The system can match and authenticate hand component imagery against a constrained custody suite reference of a known subject by employing advanced image processing and machine learning techniques. DATA: We conducted experiments on two hand datasets: Purdue University and Hong Kong. In particular, the Purdue dataset collected for this study allowed us to evaluate the system performance on various parameters, with specific emphasis on camera distance and orientation. METHODS: To explore the performance and reliability of the biometric verification models, we considered several parameters, including hand orientation, distance from the camera, single or multiple fingers, architecture of the models, and performance loss functions. RESULTS: Results showed the best performance for pictures sampled from the same database and with the same image capture conditions. CONCLUSION: The authors conclude the biometric hand verification tool offers a robust solution that will operationally impact law enforcement by allowing agencies to investigate and identify online child sexual exploitation offenders more effectively. We highlight the strength of the system and the current limitations.

TSFAN: tensorized spatial-frequency attention network with domain adaptation for cross-session EEG-based biometric recognition.

Objective.Electroencephalogram (EEG) signals are promising biometrics owning to their invisibility, adapting to the application scenarios with high-security requirements. However, It is challenging to explore EEG identity features without the interference of device and state differences of the subject across sessions. Existing methods treat training sessions as a single domain, affected by the different data distribution among sessions. Although most multi-source unsupervised domain adaptation (MUDA) methods bridge the domain gap between multiple source and target domains individually, relationships among the domain-invariant features of each distribution alignment are neglected.Approach.In this paper, we propose a MUDA method, Tensorized Spatial-Frequency Attention Network (TSFAN), to assist the performance of the target domain for EEG-based biometric recognition. Specifically, significant relationships of domain-invariant features are modeled via a tensorized attention mechanism. It jointly incorporates appropriate common spatial-frequency representations of pairwise source and target but also cross-source domains, without the effect of distribution discrepancy among source domains. Additionally, considering the curse of dimensionality, our TSFAN is approximately represented in Tucker format. Benefiting the low-rank Tucker Network, the TSFAN can scale linearly in the number of domains, providing us the great flexibility to extend TSFAN to the case associated with an arbitrary number of sessions.Main results.Extensive experiments on the representative benchmarks demonstrate the effectiveness of TSFAN in EEG-based biometric recognition, outperforming state-of-the-art approaches, as verified by cross-session validation.Significance.The proposed TSFAN aims to investigate the presence of consistent EEG identity features across sessions. It is achieved by utilizing a novel tensorized attention mechanism that collaborates intra-source transferable information with inter-source interactions, while remaining unaffected by domain shifts in multiple source domains. Furthermore, the electrode selection shows that EEG-based identity features across sessions are distributed across brain regions, and 20 electrodes based on 10-20 standard system are able to extract stable identity information.

Secure hardware IP of GLRT cascade using color interval graph based embedded fingerprint for ECG detector.

This paper presents a security aware design methodology to design secure generalized likelihood ratio test (GLRT) hardware intellectual property (IP) core for electrocardiogram (ECG) detector against IP piracy and fraudulent claim of IP ownership threats. Integrating authentic (secure version) GLRT hardware IP core in the system-on-chip (SoC) of ECG detectors is paramount for reliable operation and estimation of ECG parametric data, such as Q wave, R wave and S wave (QRS) complex detection. A pirated GLRT hardware IP integrated into an ECG detector may result in an unreliable/erratic estimation of ECG parametric data that can be hazardous and fatal for the end patient. The proposed methodology presents an integrated design flow to secure micro GLRT and GLRT cascade hardware IP cores for the ECG detector, using the colored interval graph (CIG) framework based fingerprint biometric, during high level synthesis (HLS). The proposed approach integrates a fingerprint biometric based security constraint generation process for securing the GLRT hardware IP core. This paper also presents a secure register transfer level (RTL) datapath design corresponding to micro GLRT and GLRT cascade hardware IP cores with embedded IP vendor's fingerprint. The proposed secure GLRT hardware IP core embedded with fingerprint biometric achieves superior results in terms of probability of coincidence and tamper tolerance than other security approaches. More explicitly, the proposed approach reports a significantly lower value of probability of coincidence and stronger value for tamper tolerance. Further, the proposed approach incurs zero design cost overhead.

Multi-risk factors joint prediction model for risk prediction of retinopathy of prematurity.

Purpose: Retinopathy of prematurity (ROP) is a retinal vascular proliferative disease common in low birth weight and premature infants and is one of the main causes of blindness in children.In the context of predictive, preventive and personalized medicine (PPPM/3PM), early screening, identification and treatment of ROP will directly contribute to improve patients' long-term visual prognosis and reduce the risk of blindness. Thus, our objective is to establish an artificial intelligence (AI) algorithm combined with clinical demographics to create a risk model for ROP including treatment-requiring retinopathy of prematurity (TR-ROP) infants. Methods: A total of 22,569 infants who underwent routine ROP screening in Shenzhen Eye Hospital from March 2003 to September 2023 were collected, including 3335 infants with ROP and 1234 infants with TR-ROP among ROP infants. Two machine learning methods of logistic regression and decision tree and a deep learning method of multi-layer perceptron were trained by using the relevant combination of risk factors such as birth weight (BW), gestational age (GA), gender, whether multiple births (MB) and mode of delivery (MD) to achieve the risk prediction of ROP and TR-ROP. We used five evaluation metrics to evaluate the performance of the risk prediction model. The area under the receiver operating characteristic curve (AUC) and the area under the precision-recall curve (AUCPR) were the main measurement metrics. Results: In the risk prediction for ROP, the BW + GA demonstrated the optimal performance (mean ± SD, AUCPR: 0.4849 ± 0.0175, AUC: 0.8124 ± 0.0033). In the risk prediction of TR-ROP, reasonable performance can be achieved by using GA + BW + Gender + MD + MB (AUCPR: 0.2713 ± 0.0214, AUC: 0.8328 ± 0.0088). Conclusions: Combining risk factors with AI in screening programs for ROP could achieve risk prediction of ROP and TR-ROP, detect TR-ROP earlier and reduce the number of ROP examinations and unnecessary physiological stress in low-risk infants. Therefore, combining ROP-related biometric information with AI is a cost-effective strategy for predictive diagnostic, targeted prevention, and personalization of medical services in early screening and treatment of ROP.

Relationship between body weight and hip width in dairy buffaloes (Bubalus bubalis).

The objective of the present study was to evaluate the relationship between body weight (BW) and hip width (HW) in dairy buffaloes (Bubalus bubalis). HW was measured in 215 Murrah buffaloes with a BW of 341 ± 161.6 kg, aged between three months and five years, and raised in southeastern Mexico. Linear and non-linear regressions were used to construct the prediction models. The goodness of fit of the models was evaluated using the Akaike information criterion (AIC), Bayesian information criterion (BIC), coefficient of determination (R2), mean squared error (MSE), and root MSE (RMSE). Additionally, the developed models were evaluated through internal and external cross-validation (k-folds) using independent data. The ability of the fitted models to predict the observed values was assessed based on the root mean square error of prediction (RMSEP), R2, and mean absolute error (MAE). The relationship between BW and HW showed a high correlation coefficient (r = 0.96, P < 0.001). The chosen fitted model to predict BW was: -176.33 (± 40.83***) + 8.74 (± 1.79***) × HW + 0.04 (± 0.01*) × HW2, because it presented the lowest MSE, RMSE, and AIC values, which were 1228.64, 35.05 and 1532.41, respectively. Therefore, with reasonable accuracy, the quadratic model using hip width may be suitable for predicting body weight in buffaloes.

Comparison of intraocular pressure and anterior segment parameters in subjects with asymmetrical primary angle closure disease.

BACKGROUND: To compare intraocular pressure (IOP) and anterior segment parameters between eyes with unilateral primary angle closure glaucoma (PACG) and their fellow eyes with primary angle closure (PAC) or primary angle closure suspect (PACS). METHODS: Subjects underwent anterior segment imaging using 360-degree swept-source optical coherence tomography (SS-OCT, CASIA Tomey, Nagoya, Japan) and ocular investigations including gonioscopy and IOP measurement. Each SS-OCT scan (divided into 8 frames, 22.5 degrees apart) was analysed and an average was obtained for the following anterior segment parameters: iridotrabecular contact (ITC), angle opening distance (AOD750), iris thickness and curvature, anterior chamber width, depth and area (ACW, ACD and ACA) and lens vault (LV). RESULTS: Among 132 unilateral PACG subjects (mean age: 62.91 ± 7.2 years; 59.1% male), eyes with PACG had significantly higher presenting IOP (24.81 ± 0.94 vs. 18.43 ± 0.57 mmHg, p < 0.001), smaller gonioscopic Shaffer grade (2.07 ± 0.07 vs. 2.31 ± 0.07, p < 0.001) and a greater extent of peripheral anterior synechiae (PAS, 1.21 ± 0.21 vs. 0.54 ± 0.16 clock hours, p = 0.001). PACG eyes also exhibited increased ITC, ITC area, greater LV and smaller AOD750, ACD and ACA (all p < 0.05). Using the forward stepwise regression model, an increase in 1 mmHg in presenting IOP before laser peripheral iridotomy (LPI) increases the odds of having PACG by 9% (95% confidence interval 5%-14%). CONCLUSIONS: PACG eyes have higher presenting IOP, smaller anterior segment parameters, greater extent of PAS, and larger LV compared to their fellow eyes with angle closure. Narrower anterior chamber dimensions and higher presenting IOP before LPI may increase risk of chronic elevated IOP and glaucomatous optic neuropathy after LPI.

Oxidative responses in small juveniles of Colossoma macropomum anesthetized and sedated with Ocimum gratissimum L. essential oil.

This study evaluated the use of essential oil of Ocimum gratissimum (EOOG) for anesthesia and in transport of Colossoma macropomum. Experiment 1, Test 1, anesthesia induction and recovery times were determined using different EOOG concentrations (0, 20, 50, 100, 200, 300 mg L-1), with two size classes: Juveniles I (0.86 g) and Juveniles II (11.46 g) (independent tests in a completely randomized design). Based on the results of Test 1, in Test 2 Juveniles II were exposed to EOOG concentrations: 0, 20, 100 mg L-1. Tissue samples were collected immediately after induction and 1 h post-recovery, to assess oxidative status variables. Experiment 2, Juveniles I (0.91 g) and Juveniles II (14.76 g) were submitted to transport in water with different concentrations of EOOG (0, 5, 10 mg L-1) (independent tests in a completely randomized design). The effects on oxidative status variables were evaluated. Concentrations between 50 and 200 mg L-1 EOOG can be indicated for Juveniles I, while concentrations between 50 and 100 mg L-1 EOOG for Juveniles II. The concentration of 100 mg L-1 EOOG was able to prevent oxidative damage in the liver. In Experiment 2, the concentrations of 5 and 10 mg L-1 EOOG added to the transport water caused sedation for both studied size classes of juveniles and did not cause oscillations in water quality variables nor any mortality. The concentration of 10 mg L-1 EOOG improved the oxidative status. It can be concluded that EOOG can be used for anesthesia and transport of C. macropomum.

Assessing the Impact of Tropicamide on Anterior Segment Parameters in Diabetic Patients: A Randomized Clinical Trial.

INTRODUCTION: Evaluation of anterior segment parameters is crucial in ophthalmic procedures such as intraocular surgeries and contact lens fitting. However, the use of tropicamide in diabetic patients presents challenges due to its potential impact on biometric measurements. This study aims to investigate and compare the effects of 0.5% and 1% tropicamide on anterior segment parameters in diabetic patients. METHODS: This double-masked randomized clinical trial enrolled 98 patients with diabetes mellitus. Participants were randomly assigned to receive either 0.5% or 1% tropicamide. Anterior segment parameters were measured using Pentacam HR (Oculus Optikgeräte GmbH, Wetzlar, Germany) before and 30 minutes after tropicamide administration. Parameters included anterior chamber depth (ACD), anterior chamber volume (ACV), anterior chamber angle (ACA), keratometry, central corneal thickness (CCT), white-to-white distance (WTW), and pupillary diameter (PD). RESULTS: Both concentrations of 0.5% and 1% tropicamide induced significant changes in anterior segment parameters. There was a notable increase in PD (2.99 ± 0.62, 3.11 ± 0.55, respectively, both P-values < 0.001), ACD (both 0.10 ± 0.05, both P-values < 0.001), ACV (16.69 ± 9.56, 17.51 ± 9.26, respectively, both P-values < 0.001), and WTW (0.06 ± 0.14, 0.03 ± 0.30, respectively, both P-values < 0.001), along with a decrease in ACA (-3.50 ± 10.65, -3.30 ± 6.87, P-value < 0.001 and P-value=0.001, respectively), and CCT (-6.10 ± 8.06, -6.39 ± 9.97, respectively, both P-values < 0.001) post-dilation. However, no significant changes were observed in keratometry (front Km (-0.03 ± 0.19, -0.04 ± 0.21, respectively), back Km (0.01 ± 0.05, 0.004 ± 0.05, respectively), P-values> 0.05). CONCLUSION: Both concentrations of tropicamide exhibited comparable effects on anterior segment parameters in diabetic patients. These post-dilation changes should be considered for accurate intraocular lens power calculation and decision-making for cataract, phakic intraocular lens, and refractive surgeries.

Enhancing Resilience in Biometric Research: Generation of 3D Synthetic Face Data Using Advanced 3D Character Creation Techniques from High-Fidelity Video Games and Animation.

Biometric authentication plays a vital role in various everyday applications with increasing demands for reliability and security. However, the use of real biometric data for research raises privacy concerns and data scarcity issues. A promising approach using synthetic biometric data to address the resulting unbalanced representation and bias, as well as the limited availability of diverse datasets for the development and evaluation of biometric systems, has emerged. Methods for a parameterized generation of highly realistic synthetic data are emerging and the necessary quality metrics to prove that synthetic data can compare to real data are open research tasks. The generation of 3D synthetic face data using game engines' capabilities of generating varied realistic virtual characters is explored as a possible alternative for generating synthetic face data while maintaining reproducibility and ground truth, as opposed to other creation methods. While synthetic data offer several benefits, including improved resilience against data privacy concerns, the limitations and challenges associated with their usage are addressed. Our work shows concurrent behavior in comparing semi-synthetic data as a digital representation of a real identity with their real datasets. Despite slight asymmetrical performance in comparison with a larger database of real samples, a promising performance in face data authentication is shown, which lays the foundation for further investigations with digital avatars and the creation and analysis of fully synthetic data. Future directions for improving synthetic biometric data generation and their impact on advancing biometrics research are discussed.

Toward Synthetic Physical Fingerprint Targets.

Biometric fingerprint identification hinges on the reliability of its sensors; however, calibrating and standardizing these sensors poses significant challenges, particularly in regards to repeatability and data diversity. To tackle these issues, we propose methodologies for fabricating synthetic 3D fingerprint targets, or phantoms, that closely emulate real human fingerprints. These phantoms enable the precise evaluation and validation of fingerprint sensors under controlled and repeatable conditions. Our research employs laser engraving, 3D printing, and CNC machining techniques, utilizing different materials. We assess the phantoms' fidelity to synthetic fingerprint patterns, intra-class variability, and interoperability across different manufacturing methods. The findings demonstrate that a combination of laser engraving or CNC machining with silicone casting produces finger-like phantoms with high accuracy and consistency for rolled fingerprint recordings. For slap recordings, direct laser engraving of flat silicone targets excels, and in the contactless fingerprint sensor setting, 3D printing and silicone filling provide the most favorable attributes. Our work enables a comprehensive, method-independent comparison of various fabrication methodologies, offering a unique perspective on the strengths and weaknesses of each approach. This facilitates a broader understanding of fingerprint recognition system validation and performance assessment.