Search Results (80)

Polymetallic mining severely disrupts farmland soil ecosystems, yet the vertical migration of heavy metals, interlayer pollution disparities between topsoil and deep soil, and quantitative source apportionment of composite pollutants remain poorly understood in mining–agricultural overlapping zones. Two core hypotheses were accordingly proposed: mining-derived heavy metals can migrate downward and accumulate in deep soil layers, and the coupling of geostatistical analysis and receptor modeling enables reliable differentiation between geogenic and anthropogenic pollution sources. To test these hypotheses, 512 topsoil and 148 deep soil samples were collected from the Fenghuang Mining Area for quantification of eight metals and metalloids (including As). Geostatistical approaches, the single pollution index (Pi), and Nemerow comprehensive pollution index (P_N) were utilized to characterize spatial heterogeneity and evaluate pollution severity, while a coupled PCA–PMF receptor model was adopted for quantitative source identification; vertical comparisons of element concentrations across soil profiles further validated the robustness of source apportionment outputs. The results revealed extensive heavy metal enrichment in both soil layers, with only topsoil Cd exceeding China’s risk screening value for agricultural land. Hg exhibited pronounced spatial variability and prominent anthropogenic fingerprints, and all target metals displayed consistent spatial distribution patterns along vertical soil profiles. Four distinct pollution sources were discriminated: geogenic sources dominating Cu, Zn, Cr, and Ni accumulation, mining-industrial emissions as the major contributor to Hg pollution, mixed industrial–agricultural inputs governing As and Pb enrichment, and traffic activities serving as the primary Cd source. Cd was identified as the priority pollutant threatening local farmland security. Confirmed downward percolation of anthropogenic metals creates persistent latent ecological risks across the study area, where mining and industrial discharges represent the dominant anthropogenic pollution inputs. This work systematically elucidates the geochemical signatures, vertical migration pathways, and quantitative source contributions of heavy metals in mining-disturbed farmlands, delivering solid scientific support for targeted source control, tiered risk management, and soil ecological remediation within the Fenghuang Mining Area. Moreover, the multi-method integrated analytical framework developed herein provides transferable guidance for heavy metal pollution mitigation in global polymetallic mining–agricultural regions with analogous geological and industrial backgrounds. Full article

Background/Objectives: Latent fingerprints are routinely recovered from conventional porous and non-porous substrates; however, biologically active surfaces such as plant leaves are generally regarded as unsuitable for dactyloscopic evidence. Because vegetation is frequently present at crime scenes, this study aimed to systematically evaluate whether plant leaves can retain usable friction ridge detail and to determine the durability and forensic value of such traces under laboratory and outdoor conditions. Methods: Latent fingerprints were deposited on leaves of multiple plant species (maple, ash, dandelion, bird cherry, chestnut, climbing ivy, and five-leaved ivy) under dry and hydrated conditions and at defined time intervals after deposition. Visualization was performed using several powders, with SupraNano Fluorescent Green magnetic powder providing the best performance. Developed impressions were photographed using controlled illumination and evaluated using automated quality assessment (NFIQ 2.0) and comparison software (Innovatrics IDkit 9.1.7.1004). Additional experiments examined living, growing leaves exposed to natural weather conditions for extended periods. Results: Usable ridge detail was successfully visualized on all tested species. Bottom leaf surfaces and hydrated samples generally provided better preservation and contrast. Identifiable traces persisted for up to 20 h on detached leaves and for up to 35 days on living leaves despite growth-related deformation. Under outdoor exposure, fingerprints on ivy remained visible and comparable for up to 60 days. Although overall automated quality scores were reduced by background venation, selected impressions achieved measurable comparison scores and successful matches. Conclusions: Plant leaves can serve as unconventional yet viable carriers of latent fingerprints. Magnetic fluorescent powder development combined with careful documentation enables recovery of forensically useful ridge detail even after prolonged environmental exposure. These findings expand the range of substrates that should be considered during crime scene processing and provide practical guidance for evidence collection on vegetation. Full article

The rapid growth of the Internet of Things (IoT) has increased dependency on Transport Layer Security (TLS) for securing device communications, enhancing confidentiality while reducing the visibility required by traditional intrusion detection systems. As payload inspection becomes impractical in encrypted environments, anomaly detection must instead rely on flow-level statistics and TLS metadata. This is challenging because IoT traffic is heterogeneous, non-stationary, and distributionally inconsistent across datasets, while many existing studies rely on single-dataset evaluation and therefore provide limited evidence of real-world generalisation. We introduce a TLS-aware anomaly detection framework that combines a $\beta$-Variational Autoencoder ($\beta$-VAE) with a hybrid ANOVA–Mutual Information (ANOVA–MI) feature-selection pipeline. The incremental contribution lies not in the individual use of these components, but in their integrated application to encrypted IoT anomaly detection under strict cross-dataset evaluation, where feature filtering, probabilistic latent regularisation, and threshold transferability are jointly examined without retraining or recalibration on target datasets. The framework models benign encrypted IoT traffic using probabilistic latent representations and identifies anomalies through reconstruction-error-based scoring. Network flows from the BoT-IoT, IoT-23, and ToN-IoT datasets were processed using Zeek and CICFlowMeter to construct a unified metadata feature space incorporating flow statistics and TLS attributes such as JA3 and JA3S fingerprints. The model was trained on benign BoT-IoT traffic and evaluated in both in-dataset and cross-dataset scenarios. The model achieves strong in-dataset performance on BoT-IoT (ROC-AUC $\approx 0.9996$; F1 $\approx 0.9922$) and retains robust anomaly-ranking and threshold-based detection capability under cross-dataset domain shift (IoT-23: ROC-AUC $\approx 0.9882$, F1 $\approx 0.9422$; ToN-IoT: ROC-AUC $\approx 0.9465$, F1 $\approx 0.8732$). A comparative evaluation against deterministic autoencoders and classical baselines further indicates that the proposed $\beta$-VAE achieves stronger cross-dataset anomaly-ranking performance than the compared methods. These findings support the suitability of probabilistic latent modelling for privacy-preserving anomaly detection in encrypted IoT environments. Full article

Purpose: Nano-forensics is the latest application of nano-based technology for the purpose of fingerprint detection to improve precision, expedite investigations, and enhance safety. Solid lipid nanoparticles (SLNs) represent a promising pharmaceutical nanocarrier system for different applications. This study focused on applying ZnO and/or curcumin nanoparticles (NPs) to SLNs for the purpose of fingerprint detection to improve their sensitivity, safety and selectivity. Methods: A factorial design was utilized to select the optimized Cur-SLNs and ZnO-SLNs on the basis of the smallest particle size (PS), the lowest polydispersity index (PDI) and the highest zeta potential (ZP) value. To select the safe SLN-NPs, a cytotoxicity test was applied and they were compared to the most commonly applied product in fingerprint detection. The optimized formula was investigated according to the morphological structure; confocal spectroscopy and a stability study at different storage conditions were applied. Then the SLN-NPs were evaluated for their sensitivity, efficacy and selectivity in fingerprint detection. Results: The obtained optimal Cur-SLNs and ZnO-SLNs showed a nano PS of 221.55 ± 1.34 nm and 313.950 ± 1.87 nm, respectively, a PDI value < 0.7 and a ZP > 20 mV. The cytotoxicity data demonstrate that Cur-SLNs have low toxicity, so they will be the chosen formula. TEM and Raman spectroscopy analysis of the optimized Cur-SLN formulation validated the encapsulation efficiency and structural integrity of the pharmaceutical nanosystem. Furthermore, the powder showed stability and good results with higher adherence but smudged the prints on surfaces due to the slightest moisture. Conclusions: Overall, the results confirmed that Cur-SLN nanopowders can be developed as a suggested alternative to the current toxic powders used for latent fingerprint detection in forensic science, but only after further research on various surfaces and in different conditions. Full article

Automatic detection of sensitive fields in structured data is a critical prerequisite for privacy compliance and data governance. However, existing approaches face severe cross-domain generalization challenges. Hand-crafted pattern rules often fail under highly heterogeneous naming conventions, while single statistical models tend to overfit and degrade sharply under distribution shifts between training and deployment domains. These limitations stem from the weak semantic signals and distributional heterogeneity of structured data, which make it difficult to simultaneously capture explicit rules and latent, variant-sensitive attributes. To address these challenges, we propose a detection framework based on multi-view complementary features and a Hierarchical Gated Residual Network (HGRN). The framework first constructs a full-spectrum feature system that integrates explicit rules and implicit statistical fingerprints (e.g., entropy and character texture) to fill the semantic gap. It then introduces a decision mechanism combining robust priors with dynamic residual calibration: a random forest provides a stable probabilistic anchor, which is further nonlinearly corrected by a learnable gating-and-expert network. This design explicitly resolves the cognitive conflict between rule-dominated regions and complex distributional regions. Experiments on multiple real-world datasets—including DeSSI, CMS Open Payments and Home Credit—show that the proposed method achieves a Macro-F1 of 0.9408 on DeSSI and exhibits strong in-domain performance. Under strict frozen-model cross-domain transfer, HGRN mitigates the catastrophic collapse observed in pure neural baselines and maintains moderate detection capability, offering interpretable trust allocation between rule-based priors and data-driven correction in both financial and healthcare scenarios. Full article

Latent fingerprint development on rough non-porous substrates using fingerprint powders remains challenging because surface microstructures reduce particle-adhesion selectivity and weaken the contrast between ridges and the background. In this study, Cs₂NaBi_0.6Er_0.4Cl₆ double-perovskite nanoparticles were prepared by a solvothermal method and investigated as fingerprint-development particles for latent fingerprints on frosted plastic substrates. Structural characterization by X-ray diffraction (XRD), scanning electron microscopy (SEM), Raman spectroscopy, and X-ray photoelectron spectroscopy (XPS) indicated that Er³⁺ was incorporated into the host matrix and that the product consisted of spherical nanoparticles with smooth surfaces, relatively uniform particle-size distribution, and good dispersibility. Comparative experiments involving 40 categories of latent fingerprint samples showed that the Cs₂NaBi_0.6Er_0.4Cl₆ nanoparticles outperformed conventional powders in developing fingerprints on frosted plastic substrates. Quantitative grayscale analysis using Image J 1.53K and Origin 2024 further showed that the development contrast, expressed as the D value, reached 51.21 for sebum-rich fingerprints and 35.87 for oil-contaminated model fingerprints, both of which were higher than those obtained with the other three powders. Because the fluorescence of Cs₂NaBi_0.6Er_0.4Cl₆ under UV excitation was weaker than that of the commercial red fluorescent powder, we attribute the improved development performance mainly to selective adhesion of the particles to fingerprint residues rather than to fluorescence intensity alone. In addition, the material maintained good performance for aged fingerprints within 10 days and for developed fingerprints stored for up to 8 days. These results suggest that selective residue-affinitive adhesion, possibly assisted by the hydrophilic or moisture-affinitive nature of the ionic double-perovskite particles, plays an important role in improving fingerprint development on rough non-porous substrates. This study provides a physical perspective for latent fingerprint development on rough non-porous substrates and broadens the forensic-science application of lead-free double-perovskite nanomaterials. Full article

Clinical monitoring of chronic liver disease (CLD) is currently hindered by the invasiveness of conventional biopsies. While breath-borne volatile organic compound (VOC) analysis offers a promising non-invasive alternative, the metabolic profiles of sedentary populations often overlap significantly with those of healthy individuals, making latent pathologies difficult to identify. To overcome this high-resolution diagnostic challenge, this study developed an integrated framework that couples high-performance semiconductor sensing technology with a machine learning-based analytical baseline. During the biomarker screening phase, GC-MS was utilized to analyze over 2000 VOCs, identifying 20 markers associated with CLD. These were further optimized into a robust feature panel including ammonia, isoprene, dimethyl sulfide (DMS), and limonene. For several critical metabolic features exhibiting high diagnostic potential, preliminary identifications were conducted by referencing NIST database matches and relevant literature. To maintain analytical rigor and account for the inherent complexity of trace volatile metabolites in biological samples, these signals are treated as putative metabolic features and characterized by their retention times. Regarding hardware, an InN-based sensor with Pt-AlN surface modification was fabricated, achieving a limit of detection (LOD) for ammonia below 0.2 ppm. Crucially, while the InN sensor was validated for specific core markers such as ammonia, the current AI classification model is trained on a refined 7-VOC panel derived from the comprehensive GC-MS data. To resolve diagnostic overlaps, a three-state dynamic sampling protocol (resting, exercise, and recovery) was implemented to isolate biomarkers that remain physiologically stable. By integrating multi-dimensional VOC features (e.g., isoprene and DMS) with sensor-validated data through DBSCAN and Random Forest algorithms, the framework successfully captured non-linear metabolic fingerprints. Machine learning results confirm that the framework effectively distinguished sedentary controls from CLD patients, achieving a macro-average AUC of 0.96. This integration provides a high-precision technical pathway for early-stage liver disease screening. Full article

Background: Latent fingerprints are crucial forensic evidence, but their stability can be affected by environmental factors such as direct sunlight. The findings indicate that prolonged sunlight exposure may be associated with reduced fingerprint quality and forensic usability. Methods: A total of 322 groomed latent fingerprints from one volunteer were deposited on non-porous glass and exposed to direct sunlight for 1–7 weeks. A control sample was preserved without exposure. Fingerprints were developed using magnetic powder and assessed by minutiae counts. Usability was classified according to Slovak forensic standards. Statistical analysis was conducted using the Friedman test and Durbin–Conover test. Results: Significant differences in minutiae counts were observed between the control and selected exposure intervals (weeks 1, 3, 4, 6 and 7; p < 0.05). The degradation pattern was not linear, with initial decreases followed by stabilization in later weeks. Despite statistical differences, 99.38% of fingerprints remained usable for identification, and none were classified as non-usable. Conclusions: Prolonged direct sunlight exposure did not substantially reduce the identificatory value of groomed latent fingerprints on glass. Even after several weeks, most fingerprints retained sufficient ridge detail for personal identification, supporting their evidential relevance in outdoor forensic contexts. Full article

Radio frequency fingerprints (RFFs), arising from inherent hardware imperfections, serve as distinctive features for device identification. The location- and time-dependent nature of the wireless channel directly affects RFF-based device identification, making it challenging under different channel conditions. This is primarily because the training and test datasets containing RFFs may not overlap within the same feature-space domain. In this work, the mentioned issue is addressed as a domain adaptation problem. For this objective, we propose the use of a triplet-learning-based domain-adversarial neural network within a hybrid framework named TripletDANN. We leverage the triplet loss, enabling the network to focus exclusively on device-specific latent representations under different channel conditions, while employing an adversarial loss to prevent the network from exploiting channel-specific characteristics. With this aim, data aggregation is performed together with channel labeling. The generalization capability of TripletDANN is evaluated on previously unseen test data collected across different locations under two distinct scenarios. Raw I/Q signals of 15 Wi-Fi devices are used as a case study. The proposed TripletDANN model achieves up to $88.52\%$ average device classification accuracy across the different data collection locations. On average, TripletDANN attains up to a $5\%$ performance improvement over its counterpart model. Moreover, data augmentation is employed to improve the overall performance, and a highest accuracy of $96.71\%$ is achieved on experimentally collected test data from an unseen location. 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: The recovery of latent fingerprints from submerged evidence remains a critical challenge in forensic science, as ridge details deteriorate rapidly once under water. This study aims to compare the effectiveness of three established fingerprint development techniques—cyanoacrylate fuming, small particle reagent (SPR), and powder dusting—on non-porous substrates (glass slides and stainless steel blades) immersed in different water types representative of Rajasthan’s Shekhawati region. The objective was to evaluate the influence of water composition and immersion duration on the quality and reproducibility of developed prints. Methods: Experiments were conducted under controlled laboratory conditions. Fingerprints were submerged in hard water, mineral water, and rainwater for durations of 10 min, 1 day, 5 days, and 10 days. Each condition was replicated three times. Developed fingerprints were assessed for ridge clarity using a five-point scoring scale, and the results were statistically analyzed using Chi-Square and correlation tests. Results: Cyanoacrylate fuming consistently produced the highest quality ridge detail across all submersion periods, particularly in mineral and rainwater environments. SPR exhibited moderate effectiveness, while powder dusting showed limited performance under all conditions. Statistical analysis indicated that fingerprint quality was significantly affected by water composition, substrate type, and immersion duration (p < 0.001). Conclusions: The study highlights that fingerprint recovery from submerged non-porous evidence depends strongly on water chemistry and exposure time. Cyanoacrylate fuming is confirmed as the most reliable method, while environmental variables such as ion content and water hardness play decisive roles in fingerprint preservation and visualization. Full article

Extracellular vesicles (EVs) are increasingly recognized as promising non-invasive biomarkers for cancer and other diseases, but their clinical translation remains limited by the lack of comprehensive characterization strategies. Spectroscopic approaches such as Fourier-transform infrared (FTIR) spectroscopy can provide a global biochemical fingerprint of intact EVs, but their interpretation requires advanced analytical tools. In this study, we applied an autoencoder-based framework to attenuated total reflection FTIR (ATR-FTIR) spectra of blood-derived components, including plasma, red blood cells (RBCs), RBC-ghosts, and EVs, comprising 278 samples collected from 135 patients, to obtain latent features capable of capturing biologically meaningful variability. The autoencoder compressed spectra into 12 latent features while preserving spectral information with low reconstruction error. Unsupervised UMAP projection of the latent features separated the blood components into different clusters, supporting their biological relevance. The model was then applied to EV spectra from patients with hepatocellular carcinoma (HCC) and cirrhotic controls. Four features significantly differed between the two groups, and an elastic-net regularized logistic model evaluated with a leave-one-out cross-validation framework retained a single latent feature, achieving an out-of-fold ROC AUC of 0.785 (95% CI 0.602–0.967), with performance broadly comparable to that typically reported for AFP, the most commonly used biomarker for HCC. This study provides the first proof-of-concept that an autoencoder can be applied to FTIR spectra of EVs, extracting biologically relevant latent features with potential application in cancer detection. Full article

Ni and Ni/basalt (Ni/Bst) coatings prepared by the electrodeposition on Mo substrate were analyzed with the aim of their potential application in forensics. The coatings of Ni and Ni/Bst are produced galvanostatically from the sulfamate electrolyte at different current densities and characterized by scanning electron microscope (morphology), X-ray diffraction (structure) and Vickers microindentation (microhardness). The wettability of Ni and Ni/Bst coatings was also investigated. While morphology and microhardness of the coatings strongly depended on the current density of electrodeposition and the presence of basalt particles in the electrolyte, the effect of basalt addition on structure of the coatings was not observed. The microhardness of Ni coatings was in the (1.6951–5.7246) GPa range, while the addition of basalt particles increased the range to (5.8206–10.7981) GPa. Both Ni and Ni/Bst coatings were hydrophilic, whereas comparison of the coatings obtained at the same current density showed that incorporation of the basalt particles in the coating decreases the degree of hydrophilicity, as observed by the increase in the water contact angle (WCA). The largest WCA, i.e., the smallest hydrophilicity, showed Ni/Bst coating produced at 30 mA cm⁻² (WCA ≈ 75.5°), and was about 46.7% larger than that of Mo substrate (WCA ≈ 51.5°). This coating also showed the best development of latent fingerprints with clearly visible ridge details, indicating that there is strong correlation between fingerprint development and the wettability of the coatings. Full article

Background: Artificial intelligence is transforming forensic fingerprint analysis by introducing probabilistic demographic inference alongside traditional pattern matching. This study explores how AI integration reshapes the role of forensic experts from interpreters of physical traces to epistemic corridors who validate algorithmic outputs and translate them into legally admissible evidence. Methods: A conceptual proof-of-concept exercise compares traditional AFIS-based workflows with AI-enhanced predictive models in a simulated burglary scenario involving partial latent fingermarks. The hypothetical design, which does not rely on empirical validation, illustrates the methodological contrasts between physical and algorithmic inference. Results: The comparison demonstrates how AI-based demographic classification can generate investigative leads when conventional matching fails. It also highlights the evolving responsibilities of forensic experts, who must acquire competencies in statistical validation, bias detection, and explainability while preserving traditional pattern-recognition expertise. Conclusions: AI should augment rather than replace expert judgment. Forensic practitioners must act as critical mediators between computational inference and courtroom testimony, ensuring that algorithmic evidence meets legal standards of transparency, contestability, and scientific rigor. The paper concludes with recommendations for validation protocols, cross-laboratory benchmarking, and structured training curricula to prepare experts for this transformed epistemic landscape. Full article

Soils are critical natural resources, yet their abiotic contributions to ecosystem services remain largely unexplored in valuation studies. This pilot study represents, to the best of our knowledge, the first attempt to assess the perceived value of geosystem services (GSs) from a consumer perspective. Using a discrete choice experiment with 200 respondents, we evaluated preferences for Rotella apples and Moscatello wine through mixed multinomial logit and latent class models. Results show that attributes related to soil use and soil control were consistently significant drivers of consumer utility (e.g., odds ratios of 9.38 and 5.78 for Moscatello wine and 8.46 and 5.56 for Rotella apples, respectively; p < 0.01). These attributes align more closely with the concept of a “geological fingerprint” than with existing geographical labeling schemes such as the Protected Designation of Origin. Price effects were statistically insignificant, indicating virtually no influence on choices. Both estimated models revealed preference heterogeneity and a substantial number of no-buy responses. This suggests both limited consumer familiarity with GS concepts and a limitation of our attribute descriptions, which likely failed to convey information needed for effective purchasing decisions. This study is exploratory and limited by its convenience sample, imperfect price specification, and inability to estimate willingness-to-pay measures. Nevertheless, it provides empirical support for introducing geological footprint labeling and highlights the need for improved consumer information, policy tools, and public campaigns to promote recognition and sustainable management of geodiversity in agriculture. Full article