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The frustrated total internal reflection (FTIR) optical fingerprint scanning method is widely used due to its cost-effectiveness. However, fingerprint image quality is highly dependent on fingertip surface conditions, with moisture generally considered a degrading factor. Interestingly, a prior study reported that excessive moisture may improve image quality, though their findings were based on qualitative observations, necessitating further quantitative analysis. Additionally, since the FTIR method relies on optical principles, image quality is also influenced by the wavelength of the light source. In this study, we conducted a preliminary clinical experiment to quantitatively analyze the impact of moisture levels on fingertips (wet, dry, and control) and light wavelengths (red, green, and blue) on FTIR fingerprint image quality. A total of 20 male and female participants with no physical impairments were involved. The results suggest that FTIR fingerprint image quality may improve under wet conditions and when illuminated with green and blue light sources compared to dry conditions and red light. Statistical evidence supports this consistent trend. However, given the limited sample size, the statistical validity and generalizability of these findings should be interpreted with caution. These insights provide a basis for optimizing fingerprint imaging conditions, potentially enhancing the reliability and accuracy of automatic fingerprint identification systems (AFIS) by reducing variations in individual fingerprint quality. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT)-assisted consumer electronics refer to common devices that are improved with IoT technology, allowing them to attach to the internet and convey with other devices. These smart devices contain smart home systems, smartphones, wearables, and appliances, which can be monitored remotely, gather, and share data, and deliver advanced functionalities like monitoring, automation, and real-time upgrades. Safety in IoT-assisted consumer electronics signifies a cutting-edge technique to improve device safety and user authentication. Iris recognition (IR) is a biometric authentication technique that employs the exclusive patterns of the iris (the colored part of the eye that surrounds the pupil) to recognize individuals. This method has gained high popularity owing to the uniqueness and stability of iris patterns in finance, healthcare, industries, complex systems, and government applications. With no dual irises being equal and small changes through an individual’s lifetime, IR is considered to be more trustworthy and less susceptible to exterior factors than other biometric detection models. Different classical machine learning (ML)-based IR techniques, the deep learning (DL) approach could not depend on feature engineering and claims outstanding performance. In this paper, we propose an enhanced IR using the Remora fractals optimization algorithm with deep learning (EIR-ROADL) technique for biometric authentication. The main intention of the EIR-ROADL model is to project a hyperparameter-tuned DL technique for automated and accurate IR. For securing consumer electronics, blockchain (BC) technology can be used. In the EIR-ROADL technique, the EIR-ROADL approach uses the Inception v3 method for the feature extraction procedures and its hyperparameter selection process takes place using ROA. For the detection and classification of iris images, the EIR-ROADL technique applies the variational autoencoder (VAE) model. The experimental assessment of the EIR-ROADL algorithm can be executed on benchmark iris datasets. The experimentation outcomes indicated better IR outcomes of the EIR-ROADL methodology with other current approaches and ensured better biometric authentication results. [ABSTRACT FROM AUTHOR]

Owing to its capability to offer remote services, the Internet of Things (IoT) has immersed itself in all areas of our daily lives. However, this big use of IoT networks makes the user's data change insecurely in open channels vulnerable to malicious use. As a result, the security of the user's data in an IoT environment becomes a critical issue. Given that authentication is a mechanism that may prevent hackers from retrieving and exploiting data communicated between IoT devices, researchers have proposed many lightweight IoT authentication schemes in the last decades. However, most of these schemes are based on two authentication factors and are unable to ensure unlink ability, key secrecy, perfect forward secrecy, and resistance to node capture, denial of service (DoS) attacks, stolen verifiers, denning-SSACO attacks, and GWN bypassing. In this paper, we present an anonymous three-factor authentication scheme based on elliptic curve cryptography (ECC), which can provide all security services and resist well-known attacks. Then, based on informal security analysis and the formal security proof using ProVerif we show that our provided scheme is secure and can resist known attacks. Finally, we show the comparison result among our protocol and other protocols in terms of computation overheads, communication overheads, and security features. [ABSTRACT FROM AUTHOR]

Biometric authentication relies on distinct biological traits of individuals to validate their identity, enhancing security measures. However, variations in an individual’s emotional state can impact the reliability of the biometric system. In this study, we propose a novel pipeline to evaluate electroencephalography (EEG)-based biometric system across different emotional states and optimize critical brain regions using machine learning algorithms. EEG signals from the DEAP dataset were classified into four emotional states: HAHV, HALV, LALV, and LAHV. We extracted a comprehensive set of statistical, time, frequency, entropy, fractal, spectral, and shape features from each channel. Machine learning classifiers, including Random Forest, Gradient Boosting, Extreme Gradient Boosting, LightGBM, CatBoost, and Bagging, were used for participant authentication. Our results revealed that the CatBoost classifier performed well across all stimuli with average accuracies of 84%, 85%, 86%, and 83% for HAHV, HALV, LALV, and LAHV, respectively. We found that features from channels FC1, Fz, C4 & Pz, and FC1 significantly contributed to EEG authentication on stimuli such as HAHV, HALV, LALV, and LAHV, respectively. Features such as skewness and the theta-to-alpha frequency band ratio consistently performed well across stimuli, demonstrating EEG signals’ potential for robust biometric authentication by addressing emotional variations.

As reliance on digital services grows, traditional password-based authentication methods have been increasingly scrutinized due to their susceptibility to cyber-attacks, including phishing and brute force attacks. This has led to a shift toward passwordless authentication, an approach that promises enhanced security and user experience. This paper provides a comprehensive review of passwordless authentication techniques, analysing their application in user identity verification across multiple platforms. Various methods such as biometrics, security keys, and token-based systems are explored for their efficacy in mitigating security vulnerabilities. The review highlights the advantages of passwordless authentication, including improved security, reduced user friction, and compatibility with modern identity management frameworks like FIDO2. Challenges such as usability issues, cost of deployment, and scalability are also discussed. Moreover, the paper identifies future research areas aimed at overcoming these challenges and facilitating the broad adoption of passwordless authentication in critical industries such as healthcare, finance, and public services. Future opportunities are outlined, emphasizing the need for real-world implementation, enhanced scalability, integration of AI-driven adaptive mechanisms, and innovative designs to improve user accessibility and system resilience. By consolidating existing knowledge and identifying gaps in current solutions, this study provides valuable insights for researchers and industry stakeholders seeking to enhance security through passwordless systems.

Emotions fluctuate during the process of social interaction. Although the co-creation of emotions through organizational behavior has been discussed theoretically in existing research, there is no method to demonstrate how emotions are co-created. Instead, previous studies have paid much attention to empathy, in which a person’s emotions are contagious. In contrast to self-report, which is a traditional method that can only assess emotions at a single point in time and adapts to empathy, biometric technology has made it possible to analyze emotional fluctuations over time. However, previous studies have focused only on understanding the emotional fluctuations of individuals separately. In the present study, we developed a system to measure the co-creation of emotions using a wearable device. The pulse rate was converted into valence as a positive–negative emotion, and the fluctuations in valence were analyzed by cross-correlation. We demonstrated the feasibility of the proposed system through triangulation by integrating biometrics with observation and self-report. The proposed system was verified to measure the co-creation of pair and group emotions using real-world data beyond laboratory settings. The present study contributes to business administration by proposing a critical concept for measuring the co-creation of emotions based on a constructionist approach.

This study presents a novel driver authentication system utilizing electrocardiogram (ECG) signals collected through dry electrodes embedded in the steering wheel. Traditional biometric authentication methods are sensitive to environmental changes and vulnerable to replication, but this study addresses these issues by leveraging the unique characteristics and forgery resistance of ECG signals. The proposed system is designed using autocorrelation profiles (ACPs) and a convolutional neural network and is optimized for real-time processing even in constrained hardware environments. Additionally, advanced signal processing algorithms were applied to refine the ECG data and minimize noise in driving environments. The system's performance was evaluated using a public dataset of 154 participants and a real-world dataset of 10 participants, achieving F1-Scores of 96.8% and 96.02%, respectively. Furthermore, an ablation study was conducted to analyze the importance of components such as ACPs, normalization, and filtering. When all components were removed, the F1-Score decreased to 60.1%, demonstrating the critical role of each component. These findings highlight the potential of the proposed system to deliver high accuracy and efficiency not only in vehicle environments but also in various security applications. [ABSTRACT FROM AUTHOR]

Background: Student wellness is of increasing concern in medical education. Increased rates of burnout, sleep disturbances, and psychological concerns in medical students are well documented. These concerns lead to impacts on current educational goals and may set students on a path for long-term health consequences. Methods: Undergraduate medical students were recruited to participate in a novel longitudinal wellness tracking project. This project utilized validated wellness surveys to assess emotional health, sleep health, and burnout at multiple timepoints. Biometric information was collected from participant Fitbit devices that tracked longitudinal sleep patterns. Results: Eighty-one students from three cohorts were assessed during the first semester of their M1 preclinical curriculum. Biometric data showed that nearly 30% of the students had frequent short sleep episodes (<6 hours of sleep for at least 30% of recorded days), and nearly 68% of students had at least one episode of three or more consecutive days of short sleep. Students that had consecutive short sleep episodes had higher rates of stress (8.3%) and depression (5.4%) symptoms and decreased academic efficiency (1.72%). Conclusions: Biometric data were shown to significantly predict psychological health and academic experiences in medical students. Biometrically assessed sleep is poor in medical students, and consecutive days of short sleep duration are particularly impactful as it relates to other measures of wellness. Longitudinal, biometric data tracking is feasible and can provide students the ability to self-monitor health behaviors and allow for low-intensity health interventions. [ABSTRACT FROM AUTHOR]

Emotions fluctuate during the process of social interaction. Although the co-creation of emotions through organizational behavior has been discussed theoretically in existing research, there is no method to demonstrate how emotions are co-created. Instead, previous studies have paid much attention to empathy, in which a person's emotions are contagious. In contrast to self-report, which is a traditional method that can only assess emotions at a single point in time and adapts to empathy, biometric technology has made it possible to analyze emotional fluctuations over time. However, previous studies have focused only on understanding the emotional fluctuations of individuals separately. In the present study, we developed a system to measure the co-creation of emotions using a wearable device. The pulse rate was converted into valence as a positive–negative emotion, and the fluctuations in valence were analyzed by cross-correlation. We demonstrated the feasibility of the proposed system through triangulation by integrating biometrics with observation and self-report. The proposed system was verified to measure the co-creation of pair and group emotions using real-world data beyond laboratory settings. The present study contributes to business administration by proposing a critical concept for measuring the co-creation of emotions based on a constructionist approach. [ABSTRACT FROM AUTHOR]

Biometric systems use the unique behavioral or physical characteristics of a user to verify their claimed identity. Due to the high probability of forgery or theft of traditional passwords and keys, there is a decreasing tendency to use them in security systems. By using biometric indicators, it becomes impossible to forge or steal them. Electroencephalogram (EEG) signals meet the basic requirements of biometric indicators, making them suitable for use in authentication and crypto-biometric systems. In this paper, the first step involves extracting features from recorded EEG signals using transformer-based models within an identification system. In the second step, the extracted features are imported into a key generation system. The proposed method maps the features of each user to different segments. The distributions of the segment indexes are then used to generate repeatable keys from EEG features in future sessions. The Transformer-based identification system achieved a mean accuracy of 99.8%, and the key generation system achieved a 0.1% mean Half Total Error Rate (HTER) using five different categories of visual stimulus. The high accuracy of the proposed identification system and the low error rate of the proposed key generation system indicate that features extracted by the Transformers are a good choice for visual stimulus EEG-based biometric systems. [ABSTRACT FROM AUTHOR]

Iris is one of the most widely used biometric modalities because of its uniqueness, high matching performance, and inherently secure nature. Iris segmentation is an essential preliminary step for iris-based biometric authentication. The authentication accuracy is directly connected with the iris segmentation accuracy. In the last few years, deep-learning-based iris segmentation methodologies have increasingly been adopted because of their ability to handle challenging segmentation tasks and their advantages over traditional segmentation techniques. However, the biggest challenge to the biometric community is the scarcity of open-source resources for adoption for application and reproducibility. This review provides a comprehensive examination of available open-source iris segmentation resources, including datasets, algorithms, and tools. In the process, we designed three U-Net and U-Net++ architecture-influenced segmentation algorithms as standard benchmarks, trained them on a large composite dataset (>45K samples), and created 1K manually segmented ground truth masks. Overall, eleven state-of-the-art algorithms were benchmarked against five datasets encompassing multiple sensors, environmental conditions, demography, and illumination. This assessment highlights the strengths, limitations, and practical implications of each method and identifies gaps that future studies should address to improve segmentation accuracy and robustness. To foster future research, all resources developed during this work would be made publicly available. [ABSTRACT FROM AUTHOR]

The representatives of Prunus cerasifera are the most numerous and diverse in vegetative and reproductive indicators, although valued by the local population in the sub-mountainous and mountainous regions Minkov and Mihova, 2021; Todorova and Marinova, 2024). Despite their wide distribution, this species is poorly studied. Expeditions were conducted during the period 2020-2023, in the region of the Central Stara Planina, with the aim of studying and selecting valuable plant genetic resources of cherry plum (Prunus cerasifera Ehrh) without pit and with a detachable stone. Two red-fruited genotypes of the Karlovska afozka type, 1 red-leaved, and 2 yellow large-fruited genotypes with valuable economic qualities, distinguished by high fertility and simultaneous ripening of the fruits, were selected, marked, and studied. The years of study are characterized by abundant precipitation in spring and drought during the period (July-October). The genotype Yellow 46 Troyanska stands out with the highest weight (46.74±0.35g), followed by Red Troyan Leaf (34.16g±0.48g). Regarding the biochemical composition, the highest amount of dry matter was recorded in Red Karlovska - 17.05%, and the highest value of polyphenols was registered in Red Troyan Leaf (143.27mg/g). The fruits of Red Troyan Leaf and Yellow 46 Troyanska have the most attractive appearance. [ABSTRACT FROM AUTHOR]

A Brain–Computer Interface (BCI) processes and converts brain signals to provide commands to output devices to carry out certain tasks. The main purpose of BCIs is to replace or restore the missing or damaged functions of disabled people, including in neuromuscular disorders like Amyotrophic Lateral Sclerosis (ALS), cerebral palsy, stroke, or spinal cord injury. Hence, a BCI does not use neuromuscular output pathways; it bypasses traditional neuromuscular pathways by directly interpreting brain signals to command devices. Scientists have used several techniques like electroencephalography (EEG) and intracortical and electrocorticographic (ECoG) techniques to collect brain signals that are used to control robotic arms, prosthetics, wheelchairs, and several other devices. A non-invasive method of EEG is used for collecting and monitoring the signals of the brain. Implementing EEG-based BCI technology in home automation systems may facilitate a wide range of tasks for people with disabilities. It is important to assist and empower individuals with paralysis to engage with existing home automation systems and gadgets in this particular situation. This paper proposes a home security system to control a door and a light using an EEG-based BCI. The system prototype consists of the EMOTIV Insight™ headset, Raspberry Pi 4, a servo motor to open/close the door, and an LED. The system can be very helpful for disabled people, including arm amputees who cannot close or open doors or use a remote control to turn on or turn off lights. The system includes an application made in Flutter to receive notifications on a smartphone related to the status of the door and the LEDs. The disabled person can control the door as well as the LED using his/her brain signals detected by the EMOTIV Insight™ headset. [ABSTRACT FROM AUTHOR]

Research on multi‐biometric authentication systems using multiple biometric modalities to defend against adversarial attacks is actively being pursued. These systems authenticate users by combining two or more biometric modalities using score or feature‐level fusion. However, research on adversarial attacks and defences against each biometric modality within these authentication systems has not been actively conducted. In this study, we constructed a multi‐biometric authentication system using fingerprint, palmprint, and iris information from CASIA‐BIT by employing score and feature‐level fusion. We verified the system's vulnerability by deploying adversarial attacks on single and multiple biometric modalities based on the FGSM, with epsilon values ranging from 0 to 0.5. The experimental results show that when the epsilon value is 0.5, the accuracy of the multi‐biometric authentication system against adversarial attacks on the palmprint and iris information decreases from 0.995 to 0.018 and 0.003, respectively, and the f1‐score decreases from 0.995 to 0.007 and 0.000, respectively, demonstrating susceptibility to adversarial attacks. In the case of fingerprint data, however, the accuracy and f1‐score decreased from 0.995 to 0.731 and from 0.995 to 0.741, respectively, indicating resilience against adversarial attacks. [ABSTRACT FROM AUTHOR]

This paper investigates the use of continuous morse wavelet transform (CWT) coefficients as inputs to convolutional neural networks (CNNs) for electrocardiogram (ECG) biometric authentication. We evaluate the performance and generalization of pre-trained SqueezeNet architecture using the ECG-ID Database. Our approach involves extracting 10 scalograms from each subject's ECG signals and employing gradient descent optimization during training. The models demonstrate high accuracy, achieving over 90% on both training and validation datasets, indicating robust performance and minimal overfitting. Further analysis using the F1 confidence curve and ROC curve reveals a balanced trade-off between precision and recall, with an optimal F1 score of 0.84 and an AUC of 0.84, respectively. Additionally, we explore the impact of different CWT parameter settings, including Voice per Octave (VPO), symmetry parameter (gamma), and time-bandwidth product (P2). The optimal VPO of 41 yields an AUC of 0.87 and an F1 score of 0.84. The best performance is achieved with gamma values greater than 2 and time-bandwidth products between 45 and 80, enhancing time localization and frequency resolution. In this study, the significance of fine-tuning wavelet parameters to improve the effectiveness of ECG biometric systems is demonstrated, demonstrating the potential of combining CWT and CNNs for reliable biometric authentication. [ABSTRACT FROM AUTHOR]

Next-generation identity verification using biometric features is nearly foolproof with the right classifier. However, selecting the correct classifier poses a key challenge, particularly in the recognition of biometric patterns. High-potential projects may face delays due to a lack of the right recognition mechanism or the malfunction of the selected classifier. This could also result from not choosing the appropriate classifier that aligns with the project's patterns. This study aims to evaluate various classifiers with potential in biometric research and the capabilities of different machine learning algorithms. Several classifiers were experimentally evaluated in combination with dynamic algorithms. The ultimate objective was to identify a standard classifier suitable for general biometric pattern recognition. Using well-known biometric pattern datasets, multivariate algorithms, such as Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA), were applied. These methods were combined with different classifiers, including SVM-L, MLP, KNN, etc. After analyzing the results obtained, the combination of LDA with MLP outperformed other approaches in terms of accuracy. [ABSTRACT FROM AUTHOR]

With the rapid growth of the Internet of Things (IoT), the Internet of Medical Things (IoMT) has emerged as a critical sector that enhances convenience and plays a vital role in saving lives. IoMT devices facilitate remote access and control of various medical tools, significantly improving accessibility in the healthcare field. However, the connectivity of these devices to the internet makes them vulnerable to adversarial attacks. Safeguarding medical data becomes a paramount concern, particularly when precise biometric readings are required without compromising patient safety. This paper proposes a fast encryption mechanism to protect the color information in medical videos utilized within the IoMT environment. Our approach involves scrambling medical video frames using a rapid block-splitting method combined with simple operations. Subsequently, the scrambled frames are encrypted using different keys generated from the logistic map. To ensure the practicality of our proposed method in the IoMT setting, we implement the encryption mechanism on a cost-effective Raspberry Pi platform. To evaluate the effectiveness of our proposed mechanism, we conduct comprehensive simulations and security analyses. Notably, we investigate medical test videos during the evaluation process, further validating the applicability of our method. The results confirm our proposed mechanism's robustness by hiding patterns in original videos, achieving high entropy to increase randomness in encrypted videos, reducing the correlation between adjacent pixels in encrypted videos, and resisting various attacks. [ABSTRACT FROM AUTHOR]

This study aimed to critically explore the loopholes in Branchless Banking in Pakistan. This study was qualitative, and a total of 16 cyber investigators were interviewed face-to-face from the district Faisalabad. Collected data were analyzed using the content analysis method. Results of this study are presented under different key themes, i.e. (i) serious violations of Know Your Customer (KYC) & Customer Due Diligence (CDD), (ii) BVS (Biometric Verification System) Failure followed by some sub-themes. This study unveiled that the security of internet-based financial services is a great concern. Whereas violations of KYC and CDD reflect various vulnerabilities that augment financial crimes. Ineffective use of biometric verification systems, poorly maintained retailer records, and the operation of unauthorized individuals aggravate the risks of financial crimes. There is a need for a realtime monitoring system of transactions, a strict BVS, and an awareness campaign for public on safe use of branchless banking. [ABSTRACT FROM AUTHOR]

Organic fertilizers play a significant role in organic systems and sustainable soil management. In general, organic fertilizers increase soil fertilization and can reduce the negative effects of the excessive use of chemical fertilizers and synthetic man-made fertilizers. This study designed aimed to check the comparable effect between Organic and chemical fertilizer NPK on growth traits of Dedonia viscosa. the experiment conducted in Agriculture college forestry department in plastic house during October to April 2024. RCBD full factorial used to conduct the treatment, 10 replication per treatment with three fertilizers Organic humic acid, NPK chemical and control .in April the seedlings harvested, the biometric traits height and diameter measured in seedlings and biomass trait. The results show highly significancy about effect of plastic house conditions temperature and light on biometric growth of seedlings were p value ≤0.001 between D1 and D2 during the 6 months of experiment with increment 53 cm in height of seedling. And the results show significance between fertilizers effect on biometric and biomass growth traits (shoot biomass and root biomass) and seedling diameter if the p value were p value ≤0.05.so the data p value for the growth trait (Shoot biomass) for the seedlings showed significant P value ≤0.05 and ≤0.001 during the experiment duration between NPK and Humic acid and control. and the p value of anova table were ≥0.05 between NPK and Control and ≤0.001 between Control and humic acid. Cording to these results we recommend to propagate dedonia viscosa inside the plastic house to keep him from winter low temperature and wind outside and even we recommend to use organic humic acid fertilizer 10ml/ltr conc, even the NPK chemical can be used in 800 ppm conc and it give good results but less than humic acid which is get better growth traits biomet

Estimation of gender, hand, and finger to minimize the probable suspects list in a fingerprint database search is a very important stride in forensic anthropology. Previous research attempted to estimate the gender, hand, and finger from the fingerprint, but the results were not consistent. In this effort, we proposed gender, hand, and finger estimation based on fingerprints using a deep convolution neural network. The publicly available SOCOFIG dataset which embraces 55222 no of fingerprints, is used for training and evaluation of the proposed procedure. On the aforementioned dataset, the suggested mode of operation achieves 99.38\% gender, 99.46\% hand, and 97.36\% finger prediction validation accuracy. The results are competitive and commendable when compared to the preceding techniques.

Abstract 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.

In the profession of policing, the accumulation of stressful incidents over the course of a career can lead to a host of adverse health outcomes: increased incidence of injury and illness, diminished cognitive performance, mental health impacts (including anxiety, depression, addiction and elevated risk of suicide), increased risk of cardiovascular disease and early mortality. The toxic climate of dysfunctional agency culture, local community resistance and distrust, and the national political discourse around policing all serve to increase the stress that first responders bear, contributing to erosion of police-community relationships. Beyond Us & Them partnered with California State University San Marcos to offer the Peace Officer Wellness, Empathy & Resilience (POWER) training to university police officers. POWER is a nationally certified 12-week training program, which teaches skills and practices that promote well-being, mindfulness and relationality, and improve police-community relations. Based on survey data from prior cohorts, we realized the potential benefit of adding biometric measurements to look for improvement in autonomic health. Other studies have demonstrated an inverse correlation between heart rate variability (HRV) and cardiovascular disease, cognitive decline and risk of allcause mortality. Of the 17 participants, 15 completed preand post-intervention surveys, and HRV was obtained from 13 of these participants: findings demonstrated improved autonomic health, as well as statistically significant changes in empathy, mindfulness and social connection. Additionally, we noted increased HRV coherence, which may be a physiologic marker of enhanced social connection. Future studies offer the possibility of utilizing HRV coherence as a marker of group connection and performance. [ABSTRACT FROM AUTHOR]

In this study, an approach to the phylogeny of Nummulites was made by performing biometric analysis on measurements of Nummulites forms collected from sedimentary environments of Cuisian - early Lutetian (early - middle Eocene) age outcropping in the Fındıcak and Yenisölöz localities (northwestern Anatolia) in northwestern Turkey. The Cuisian (early Eocene) aged Fındıcak Formation, characterized by carbonate-dominated conglomerate, sandstone, siltstone, limestone, and marl sequences, the early Lutetian (middle Eocene) aged Dürdane Formation, and the Priabonian (late Eocene) aged Soğucak Formation were examined for macro- and microspheric forms of Nummulites individuals. The biometric analysis of the macro- and microspheric forms of the collected Nummulites individuals with radial-granular and radial-white spotted characteristics identified the species Nummulites burdigalensis de la Harpe (Cuisian) and Nummulites uranensis de la Harpe (early Lutetian). A table was created containing the breakdown of the data for the two localities based on biometric measurements. As a result of the overlapping distributions of the two species, some clues were obtained about the genus characteristics of Nummulites, their developmental stages starting from the initial locus, and the changes they underwent until becoming adult individuals. Considering these changes, the phylogenetic development of the Nummulites from the study area, represented within the underlined species, was revealed as the lineage Nummulites burdigalensis → uranensis. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

A system that recognizes the iris is susceptible to presentation attacks (PAs), in which a malicious party shows artefacts such as printed eyeballs, patterned contact lenses, or cosmetics to obscure their personal identity or manipulate someone else's identity. In this study, we suggest the dual channel DenseNet presentation attack detection (DC-DenseNetPAD), an iris PA detector based on convolutional neural network architecture that is dependable and effective and is known as DenseNet. It displays generalizability across PA datasets, sensors, and artifacts. The efficiency of the suggested iris PA detection technique has been supported by tests performed on a popular dataset which is openly accessible (LivDet-2017 and LivDet-2015). The proposed technique outperforms state-of-the-art techniques with a true detection rate of 99.16% on LivDet-2017 and 98.40% on LivDet-2015. It is an improvement over the existing techniques using the LivDet-2017 dataset. We employ Grad-CAM as well as t-SNE plots to visualize intermediate feature distributions and fixat. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Fingerprint and iris traits are used in sensitive applications and so, spoofing them can impose a serious security threat as well as financial damages. Spoofing is a process of breaking biometric security using artificial biometric traits. This spoofing can be avoided by detecting the liveness of the biometric traits. Hence, liveness detection techniques have become an active research area. However, liveness detection techniques are also prone to attack because of advanced spoofing materials. Hence, they are subjected to further development to face futuristic spoofing and compromising real biometric traits. To aid the development, this paper technically and informatically reviews the state-of-the-art liveness detection techniques in the last decade. Firstly, the paper reviews the processing strata, adopted features and detectors in the existing liveness detection techniques. Secondly, the paper presents the benchmark datasets, their characteristics, availability and accessibility, along with the potential spoofing materials that have been reported in the literature under study. Thirdly, the survey reports the performance of the techniques on the benchmark datasets. Eventually, this paper summarizes the findings, gaps and limitations to facilitate strengthening of liveness detection techniques. This paper further reports that the Fingerprint Liveness Detection (FLD) techniques such as Slim-ResCNN, JLW and Jung CNN have achieved a better accuracy of 94.30%, 98.61% and 97.99%, respectively on LivDet19 datasets. It has been observed that CNN-based architectures have outperformed in significant number of FLD datasets. In contrast, Support Vector Machine (SVM) with appropriate shallow and deep features has achieved equivalent performance against deep classifiers on detecting iris spoofs from Iris Liveness Detection (ILD) datasets. [ABSTRACT FROM AUTHOR]

The blockchain has continued to showcase myriads of promises as a breakthrough tech and highly adapted in manifold sectors. Its adoption is accompanied by a range of issues that make implementation complicated. To aid successful implementation, a variety of frameworks have been developed. But, selecting the appropriate framework based on the conformity of its features with the financial sector is a challenge for decision-makers. This study seeks to provide solution to the challenge of synchronizing entire blockchain in a minimum possible time, and create a Blockchain Technology Framework for pricing and Evaluating Financial Markets and also simulate the cryptocurrency market data using a neural network based application to generate the optimal prediction and predicting the future of investment by fusing the facial recognition system with the framework. [ABSTRACT FROM AUTHOR]

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Purpose: The purpose of this study is to highlight and demonstrate how the study of stress and related responses in construction can best be measured and benchmarked effectively. Design/methodology/approach: A range of perceptual and physiological measures are obtained across different time periods and during different activities in a fieldwork setting. Differences in the empirical results are analysed and implications for future studies of stress discussed. Findings: The results of this study strongly support the use of multiple psychometrics and biosensors whenever biometrics are included in the study of stress. Perceptual, physiological and environmental factors are all shown to act in concert to impact stress. Strong conclusions on the potential drivers of stress should then only be considered when consistent results apply across multiple metrics, time periods and activities. Research limitations/implications: Stress is an incredibly complex condition. This study demonstrates why many current applications of biosensors to study stress in construction are not up to the task and provides empirical evidence on how future studies can be significantly improved. Originality/value: To the best of the author's knowledge, this is the first study to focus explicitly on demonstrating the need for multiple research instruments and settings when studying stress or related conditions in construction. [ABSTRACT FROM AUTHOR]