Search Results (358)

The dairy sheep, vital to the Mediterranean economy, struggles to balance productivity, sustainability, and animal welfare, especially in extensive, small-scale systems. Precision livestock farming (PLF) technologies offer new opportunities by enabling continuous, non-invasive, and data-driven monitoring across diverse farming conditions. Despite rapid progress in sensors, computer vision, wearable devices, and artificial intelligence (AI), a comprehensive synthesis focused on dairy sheep remains limited. This review provides an updated overview of PLF applications in dairy sheep farming, based on a literature review. The 2018–2025 timeframe was chosen to capture recent advances in Internet of Things (IoT), AI, and sensor technologies that have achieved practical relevance only in recent years. The review identifies core technological domains such as automated weight and body condition monitoring, biometric identification, wearable and IoT-based sensors, localization systems, behavioral and thermal monitoring, virtual fencing, drone-assisted herding, and advanced decision-support tools. Innovations including lightweight deep-learning models, multimodal sensing frameworks, and digital twins highlight the growing potential for scalable, real-time applications. While technological progress is substantial, practical adoption is hindered by economic, technical, interoperability, and ethical barriers. This review consolidates current evidence and identifies future priorities to guide the development of integrated, welfare-focused PLF solutions for dairy sheep farming. Full article

Global warming poses a threat to food security, particularly for essential crops like cowpea, which exhibits sensitivity to heat stress. This study aimed to evaluate the morpho-agronomic diversity of cowpea genotypes under different daily temperature regimes. The experiment was conducted in growth chambers, and biometric and productive traits were measured to quantify genetic divergence using Mahalanobis distance and UPGMA clustering. Temperature increases markedly altered trait expression. Under the 20–26–33 °C regime, 100-grain weight, leaf dry weight, pod weight, and stem dry weight accounted for 54.44% of the total variation. Under the higher temperature regime (24.8–30.8–37.8 °C), number of pods, plant height, stem fresh weight, and leaf dry weight explained 67.27% of the diversity, evidencing the impact of heat stress on vegetative and productive traits. Cluster analysis identified five distinct groups, confirming genetic variability and temperature-dependent dissimilarity patterns. Genotypes Bico de Ouro 17-53, Bico de Ouro 17-33 and BRS Tumucumaque maintained higher grain number and grain weight under elevated temperatures, whereas others showed yield reductions of up to 65%. These findings demonstrate exploitable genetic variability for heat tolerance in cowpea and support the use of morpho-agronomic traits as effective criteria for selecting genotypes adapted to warmer environments. Full article

Background/Objectives: This systematic review examines how artificial intelligence (AI) is transforming fingerprint and latent print identification in criminal investigations, tracing the evolution from traditional dactyloscopy to Automated Fingerprint Identification Systems (AFISs) and AI-enhanced biometric pipelines. Methods: Following PRISMA 2020 guidelines, we conducted a literature search in the Scopus, Web of Science, PubMed/MEDLINE, and legal databases for the period 2000–2025, using multi-step Boolean search strings targeting AI-based fingerprint identification; 68,195 records were identified, of which 61 peer-reviewed studies met predefined inclusion criteria and were included in the qualitative synthesis (no meta-analysis). Results: Across the included studies, AI-enhanced AFIS solutions frequently demonstrated improvements in speed and scalability and, in several controlled benchmarks, improved matching performance on low-quality or partial fingerprints, although the results varied depending on datasets, evaluation protocols, and operational contexts. They also showed a potential to reduce certain forms of examiner-related contextual bias, while remaining susceptible to dataset- and model-induced biases. Conclusions: The evidence indicates that hybrid human–AI workflows—where expert examiners retain decision making authority but use AI for candidate filtering, image enhancement, and data structuring—currently offer the most reliable model, and emerging developments such as multimodal biometric fusion, edge computing, and quantum machine learning may contribute to making AI-based fingerprint identification an increasingly important component of law enforcement practice, provided that robust regulation, continuous validation, and transparent governance are ensured. Full article

Background/Objectives: Subjective refraction is the clinical gold standard but is time-consuming and examiner-dependent. Most artificial intelligence (AI)-based approaches rely on specialized imaging or biometric data not routinely available. This study aimed to predict subjective refraction using only routine, non-cycloplegic autorefraction and keratometric data and to identify factors associated with reduced prediction accuracy. Methods: This retrospective study included 1856 eyes from 1006 patients. A multi-output histogram gradient-boosting model predicted subjective spherical equivalent, cylindrical power, and astigmatic axis. Performance was evaluated on an independent test dataset using R² and mean absolute error, with circular statistics for axis prediction. Prediction failure was assessed using clinically relevant tolerance thresholds (sphere/cylinder ≤ 0.50 D; axis ≤ 10°) and multivariable logistic regression. Results: The model achieved high accuracy for spherical and cylindrical prediction (R² = 0.987 and 0.933; MAE = 0.126 D and 0.137 D). Astigmatic axis prediction demonstrated strong circular agreement (ρ = 0.898), with a mean absolute angular error of 4.65° (median, 0.96°). Axis errors were higher in eyes with low cylinder magnitude (<0.75 D) and oblique astigmatism. In multivariable analysis, steeper keratometry (K2; OR = 7.25, 95% CI 1.62–32.46, p = 0.010) and greater objective cylindrical power (OR = 2.79, 95% CI 1.87–8.94, p = 0.032) were independently associated with poor prediction. Conclusions: A machine-learning model based solely on routine, non-cycloplegic autorefractor and keratometric measurements can accurately estimate subjective refraction, supporting AI as a complementary decision-support tool rather than a replacement for conventional subjective refraction. Full article

The ubiquity of smartphones has transformed them into primary repositories of sensitive data; however, traditional one-time authentication mechanisms create a critical trust gap by failing to verify identity post-unlock. Our aim is to mitigate these vulnerabilities and align with the Zero Trust Architecture (ZTA) framework and philosophy of “never trust, always verify,” as formally defined by the National Institute of Standards and Technology (NIST) in Special Publication 800-207. This study introduces a robust continuous authentication (CA) framework leveraging multimodal behavioral biometrics. A dedicated application was developed to synchronously capture touch, sliding, and inertial sensor telemetry. For feature modeling, a heterogeneous deep learning pipeline was employed to capture modality-specific characteristics, utilizing Convolutional Neural Networks (CNNs) for sensor data, Long Short-Term Memory (LSTM) networks for curvilinear sliding, and Gated Recurrent Units (GRUs) for discrete touch. To resolve performance degradation caused by class imbalance in Zero Trust environments, a Grid Search Optimization (GSO) strategy was applied to optimize a weighted voting ensemble, identifying the global optimum for decision thresholds and modality weights. Empirical validation on a dataset of 35,519 samples from 15 subjects demonstrates that the optimized ensemble achieves a peak accuracy of 99.23%. Sensor kinematics emerged as the primary biometric signature, followed by touch and sliding features. This framework enables high-precision, non-intrusive continuous verification, bridging the critical security gap in contemporary mobile architectures. Full article

This study aims to develop and demonstrate a low-cost LiDAR-based 3D mapping approach for estimating tree carbon stock in university campuses. Unlike conventional field-based measurements, which are labor-intensive and error-prone, the proposed system integrates a 2D LiDAR sensor with a servo motor and odometry data to generate three-dimensional point clouds of trees. From these data, key biometric parameters such as diameter at breast height (DBH) and total height are automatically extracted and incorporated into species-specific and generalized allometric equations, in line with IPCC 2006/2019 guidelines, to estimate above-ground biomass, below-ground biomass, and total carbon storage. The experimental study is conducted over approximately 70,000 m² of green space at Gazi University, Ankara, where six dominant species have been identified, including Cedrus libani, Pinus nigra, Platanus orientalis, and Ailanthus altissima. Results revealed a total carbon stock of 16.82 t C, corresponding to 61.66 t CO₂eq. Among species, Cedrus libani (29,468.86 kg C) and Ailanthus altissima (13,544.83 kg C) showed the highest contributions, while Picea orientalis accounted for the lowest. The findings confirm that the proposed system offers a reliable, portable, cost-effective alternative to professional LiDAR scanners. This approach supports sustainable campus management and highlights the broader applicability of low-cost LiDAR technologies for urban carbon accounting and climate change mitigation strategies. Full article

The purpose of this research was to investigate whether music empirically associated with therapeutic effects contains intrinsic informational structures that differentiate it from other sound sequences. Drawing on ontology, phenomenology, nonlinear dynamics, and complex systems theory, we hypothesize that therapeutic relevance may be linked to persistent structural patterns embedded in musical signals rather than to stylistic or genre-related attributes. This paper introduces the Discriminating Music Sequences (DiMuSes) method, an unsupervised, structure-oriented analytical framework designed to detect such patterns. The method applies 24 scalar evaluators derived from statistics, fractal geometry, nonlinear physics, and complex systems, transforming sound sequences into multidimensional vectors that characterize their global temporal organization. Principal Component Analysis (PCA) reduces this feature space to three dominant components (PC1–PC3), enabling visualization and comparison in a reduced informational space. Unsupervised k-Means clustering is subsequently applied in the PCA space to identify groups of structurally similar sound sequences, with cluster quality evaluated using Silhouette and Davies–Bouldin indices. Beyond clustering, DiMuSe implements ranking procedures based on relative positions in the PCA space, including distance to cluster centroids, inter-item proximity, and stability across clustering configurations, allowing melodies to be ordered according to their structural proximity to the therapeutic cluster. The method was first validated using synthetically generated nonlinear signals with known properties, confirming its capacity to discriminate structured time series. It was then applied to a dataset of 39 music and sound sequences spanning therapeutic, classical, folk, religious, vocal, natural, and noise categories. The results show that therapeutic music consistently forms a compact and well-separated cluster and ranks highly in structural proximity measures, suggesting shared informational characteristics. Notably, pink noise and ocean sounds also cluster near therapeutic music, aligning with independent evidence of their regulatory and relaxation effects. DiMuSe-derived rankings were consistent with two independent studies that identified the same musical pieces as highly therapeutic.The present research remains at a theoretical stage. Our method has not yet been tested in clinical or experimental therapeutic settings and does not account for individual preference, cultural background, or personal music history, all of which strongly influence therapeutic outcomes. Consequently, DiMuSe does not claim to predict individual efficacy but rather to identify structural potential at the signal level. Future work will focus on clinical validation, integration of biometric feedback, and the development of personalized extensions that combine intrinsic informational structure with listener-specific response data. Full article

Orchard crops generate substantial quantities of diverse biomass each year, with grapevines being among the most economically significant species worldwide. Considering the scale of this biomass, there is a growing need to explore rational strategies for its utilisation, for example, for energy production or other value-added applications. Such approaches may contribute to improving resource efficiency and reducing the environmental burden associated with agricultural waste. The aim of this study was to examine the energy potential of woody post-production waste from wine processing, with particular emphasis on grape stems of four cultivars—Chardonnay, Riesling, Merlot, and Zweigelt—grown in two contrasting climatic regions: south-eastern Poland and Moravia (Czech Republic). The results demonstrated that both the grape variety and cultivation site significantly influenced the majority of bunch biometric traits, including bunch and berry weight, berry number, and stem dimensions. A moderately warm climate promoted the development of larger and heavier bunches as well as more robust stems across all examined cultivars. Energy analyses indicated that Zweigelt stems produced under moderately warm conditions and Chardonnay stems from a temperate climate exhibited the most favourable combustion properties. Nonetheless, certain constraints were identified, such as increased ash (12.20%) and moisture content (11.51%) in Chardonnay grown in warmer conditions, and elevated CO and CO₂ emissions observed for Zweigelt (1333.26 kg·mg⁻¹). Overall, the findings confirm that grape stems constitute a promising local source of bioenergy, with their energy performance determined predominantly by varietal characteristics and climatic factors. Their utilisation aligns with circular-economy principles and may help reduce the environmental impacts associated with traditional viticultural waste management. Full article

The domestic donkey (Equus asinus) remains an essential component of agricultural systems worldwide, particularly in rural Tunisia. This study aims to conduct a comprehensive morphological characterization of the Tunisian donkey population, focusing on phaneroptic variation, sexual dimorphism and live weight prediction. A total of 556 clinically healthy donkeys, consisting of 207 males and 349 females, were sampled from various governorates in Tunisia. Descriptive statistics revealed significant variability in withers height (98–147 cm), thoracic circumference (100–165 cm) and body length (86–144 cm). Principal Component Analysis identified coat color and belly pigmentation as major contributors to phaneroptic variation, explaining 69.556% of the total variance. Hierarchical Ascendant Classification further classified the population into three distinct groups, with Group I exhibiting smaller body dimensions, Group II having medium sizes, and Group III consisting of larger donkeys. Sexual dimorphism was detected in neck circumference (females: 64.30 cm vs. males: 61.97 cm; FDR-adjusted p = 0.0468). Regression models for predicting live weight indicated that thoracic circumference was the most reliable single predictor (R² = 95.4%). Overall, the study documents a wide range of morpho-biometric variation within the Tunisian donkey population and provides practical tools for field-based weight estimation, offering valuable insights for future conservation strategies and management programs. Full article

Salt stress is one of the most harmful abiotic stresses that strongly affects plant growth and crop yield, limiting agricultural production across the Mediterranean area. Consequently, there is a growing need to identify resilient crops capable of adapting to saline conditions and enhancing desirable qualitative traits through a wide spectrum of physiological, biochemical, and molecular mechanisms. Therefore, this study aimed to investigate the effects of four different NaCl concentrations (0, 12.5, 25, and 50 mM) on the growth rates, biometric and productive characteristics, leaf gas exchange, and biochemical traits of Stevia rebaudiana Bertoni plants grown hydroponically (in a floating raft system) in a glasshouse. The results showed that NaCl-treated plants exhibited reduced growth parameters and productivity and a lower content of photosynthetic pigment content compared to the control. On the other hand, an increase in antioxidant capacity was observed due to the significant accumulation of total phenols and flavonoids, especially when stevia plants were treated with 50 mM NaCl. Similarly, the leaf concentration of ascorbic acid and glutathione remarkably increased. This provides new insight into the antioxidant defense strategy of S. rebaudiana under salt stress, demonstrating that stevia plants rely mainly on non-enzymatic mechanisms to counter oxidative stress. Although the highest salinity level (50 mM NaCl) resulted in the lowest content of steviol glycosides (stevioside + rebaudioside A), plants treated with 25 mM NaCl showed both the highest rebaudioside A content and Reb A/Stev ratio, which are desirable properties for the production of high-quality natural sweeteners. Overall, these findings underline that stevia can be considered a moderately salt-tolerant species, and mild stress conditions are able to promote the biosynthesis of interesting secondary metabolites, such as polyphenols and rebaudioside A. Full article

Smart virtual reality (VR) systems are becoming central to media production education, where immersive practice, real-time feedback, and hands-on simulation are essential. This review synthesizes the integration of artificial intelligence (AI) into human-centered, interactive VR learning for television and media production. Searches in Scopus, Web of Science, IEEE Xplore, ACM Digital Library, and SpringerLink (2013–2024) identified 790 records; following PRISMA screening, 94 studies met the inclusion criteria and were synthesized using a systematic scoping review approach. Across this corpus, common AI components include learner modeling, adaptive task sequencing (e.g., RL-based orchestration), affect sensing (vision, speech, and biosignals), multimodal interaction (gesture, gaze, voice, haptics), and growing use of LLM/NLP assistants. Reported benefits span personalized learning trajectories, high-fidelity simulation of studio workflows, and more responsive feedback loops that support creative, technical, and cognitive competencies. Evaluation typically covers usability and presence, workload and affect, collaboration, and scenario-based learning outcomes, leveraging interaction logs, eye tracking, and biofeedback. Persistent challenges include latency and synchronization under multimodal sensing, data governance and privacy for biometric/affective signals, limited transparency/interpretability of AI feedback, and heterogeneous evaluation protocols that impede cross-system comparison. We highlight essential human-centered design principles—teacher-in-the-loop orchestration, timely and explainable feedback, and ethical data governance—and outline a research agenda to support standardized evaluation and scalable adoption of smart VR education in the creative industries. Full article

Wearable technologies are increasingly integrated into digital health systems to support continuous remote monitoring in oncology; however, the lack of standardized and reproducible criteria for device selection limits their scalable and regulation-compliant adoption in clinically oriented infrastructures. This study proposes a preclinical benchmarking framework for the systematic evaluation of commercially available wearable devices for oncology applications. Devices were assessed across six predefined dimensions: biometric data acquisition, application programming interface-based interoperability, regulatory compliance, battery autonomy, cost, and absence of mandatory subscription fees. From an initial pool of 23 devices, a stepwise screening process identified 6 eligible wearables, which were compared using a semi-quantitative weighted scoring system. The benchmarking analysis identified the Withings ScanWatch 2 as the highest-ranked device, achieving a score of 37/40 and representing the only solution combining medical-grade certification for selected functions, extended battery life (up to 30 days), declared General Data Protection Regulation-compliant data governance, and fully accessible application programming interfaces. The remaining devices scored between 17 and 23 due to limitations in certification, battery autonomy, or data accessibility. This work introduces a reproducible preclinical benchmarking methodology that supports transparent wearable device selection in oncology and provides a foundation for future scalable digital health integration under appropriate regulatory and interoperability governance. Full article

The “Chiffchaff complex” is a group of species with several similar subspecies, whose post-breeding distribution remains poorly understood, particularly in southern Europe. This study combines morphological and molecular approaches to investigate the subspecific composition and phenological patterns of Chiffchaffs captured in eastern Sicily. A total of 380 individuals were biometrically measured, with particular focus on wing length of P8 feather, and 81 individuals were genetically analyzed using ND2 mitochondrial marker. Morphological analysis highlighted significant variation in P8 length between phenological groups. Assuming that a turnover of individuals from different origins may occur in the study area, genetic investigation was deemed necessary to further investigate this high morphological diversity. Phylogenetic analysis revealed high intraspecific genetic diversity and identified two subspecies in the study area: P. c. collybita (73%) and P. c. abietinus (27%). These findings genetically confirm for the first time the presence of P. c. abietinus in Sicily and suggest a complex pattern of seasonal co-occurrence between populations. This work contributes to the understanding of Chiffchaff migration ecology and underlines the importance of integrating ringing data with molecular tools in Mediterranean biodiversity hotspots. Full article

Score fusion is a technique that combines the matching scores from multiple biometric modalities for an authentication system. Biometric modalities are unique physical or behavioral characteristics that can be used to identify individuals. Biometric authentication systems use these modalities to verify or identify individuals. Score fusion can improve the performance of biometric authentication systems by exploiting the complementary strengths of different modalities and reducing the impact of noise and outliers from individual modalities. This paper proposes a new score fusion method based on the Sine Cosine Algorithm (SCA). SCA is a meta-heuristic optimization algorithm used in various optimization problems. The proposed method extracts features from multiple biometric sources and then computes intra/inter scores for each modality. The proposed method then normalizes the scores for a given user using different biometric modalities. Then, the mean, maximum, minimum, median, summation, and Tanh are used to aggregate the scores from different biometric modalities. The role of the SCA is to find the optimal parameters to fuse the normalized scores. We evaluated our methods on the CASIA-V3-Internal iris dataset and the AT&T (ORL) face database. The proposed method outperforms existing optimization-based methods under identical experimental conditions and achieves an Equal Error Rate (EER) of 1.003% when fusing left iris, right iris, and face. This represents an improvement of up to 85.89% over unimodal baselines. These findings validate SCA’s effectiveness for adaptive score fusion in multimodal biometric systems. Full article

Biometric authentication is widely regarded as more secure and reliable than conventional approaches like passwords and PINs. Nonetheless, many current systems rely on active user participation, such as fingerprint scanning or facial recognition, which can disrupt tasks, increase the likelihood of errors, and raise privacy concerns. To address these challenges, this study introduces a continuous, seamless authentication framework that utilizes vital signs for passive identity verification across various activities, including resting, walking, and running. The framework analyzes physiological indicators such as Heart Rate (HR), Heart Rate Variability (HRV), Skin Temperature, Peripheral Oxygen Saturation (${\mathrm{SpO}}_2$), and Breathing Rate to provide zero-effort authentication without requiring user intervention. Multiple machine learning algorithms, including Decision Tree, Random Forest, XGBoost, Gradient Boosting, and K-Nearest Neighbors, were implemented and compared to identify the most effective predictive model. The methodology involved data collection, preprocessing, model construction, evaluation, and comparison. Experimental results revealed that the XGBoost Classifier achieved the highest accuracy at 96%. Overall, the proposed framework demonstrates strong reliability, scalability, adaptability, and flexibility, making it suitable for practical deployment. By continuously verifying identity without interrupting user activity, it improves both security and usability, offering a modern and convenient alternative to traditional authentication methods. Full article