Search Results (64)

This study presents a computational analysis of industry-specific advertising message strategies through the theoretical lens of the FCB (Foote, Cone & Belding) grid framework. Leveraging the AiSAC (AI Analysis System for Ad Creation) system developed by the Korea Broadcast Advertising Corporation (KOBACO), we analyzed 27,000 Korean advertisements across five major industries using advanced machine learning techniques. Through Latent Dirichlet Allocation topic modeling with a coherence score of 0.78, we identified five distinct message strategies: emotional appeal, product features, visual techniques, setting and objects, and entertainment and promotion. Our computational analysis revealed that each industry exhibits a unique “message strategy fingerprint” that significantly discriminates between categories, with discriminant analysis achieving 62.7% classification accuracy. Time-series analysis using recurrent neural networks demonstrated a significant evolution in strategy preferences, with emotional appeal increasing by 44.3% over the study period (2015–2024). By mapping these empirical findings onto the FCB grid, the present study validated that industry positioning within the grid’s quadrants aligns with theoretical expectations: high-involvement/think (IT and Telecom), high-involvement/feel (Public Institutions), low-involvement/think (Food and Household Goods), and low-involvement/feel (Services). This study contributes to media science by demonstrating how computational methods can empirically validate the established theoretical frameworks in advertising, providing a data-driven approach to understanding message strategy patterns across industries. Full article

Optical coherence tomography (OCT) employs light to acquire high-resolution 3D images and is widely applied in fields such as ophthalmology and forensic science. A popular technique for visualizing the top view (en face) is to slice it with flat horizontal plane or apply statistical functions along the depth axis. However, when the target appears as a thin layer, strong reflections from other layers can interfere with the target, rendering the flat-plane approach ineffective. We apply Otsu-based thresholding to extract the object’s foreground, then use least squares (with Tikhonov regularization) to fit a polynomial curve that describes the sample’s structural morphology. The surface is then used to obtain the latent fingerprint image and its residues at different depths from a translucent tape, which cannot be analyzed using conventional en face OCT due to strong reflection from the diffusive surface, achieving FSIM of 0.7020 compared to traditional en face of 0.6445. The method is also compatible with other signal processing techniques, as demonstrated by a thermal-printed label ink thickness measurement confirmed by a microscopic image. Our approach empowers OCT to observe targets embedded in samples with arbitrary postures and morphology, and can be easily adapted to various optical imaging technologies. Full article

Papilloscopy, the science of human identification through fingerprints, has seen notable advancements in developing less toxic latent fingerprint developers (LFDs), especially from natural feedstock. Tannins, the second most abundant natural polyphenol, present a potential eco-friendly and cost-effective alternative, with no record of their use as LFDs in the existing literature. This study characterized four types of tannins from black wattle, using Fourier Transform Infrared Spectroscopy, revealing key functional groups like C=O, C=C, and O–H. Ultraviolet–visible absorption spectra showed similar behaviors for all tannins, indicating phenolic and benzenoid structures. Energy-dispersive X-ray Spectroscopy identified high concentrations of chlorine, sodium, potassium, and sulfur, naturally found in biomass and soil. Finally, elements in significant concentrations, such as sodium, potassium, iron, zinc, and copper, were found through the incineration of the spent bark. On the basis of these findings, the tannin with the highest potential for LFD was selected. Combining this tannin with spent bark ash resulted in a composite whose performance was evaluated using different methods, including depletion studies, tests with various donors, and assessments on different surfaces. The results demonstrated that this combination significantly enhanced the material’s efficiency by integrating organic and inorganic properties, which improved visual contrast and powder adhesion. Full article

In large-scale studies, uncontrolled systematic variability introduced during sample preparation, processing, and storage can interfere with the detection of subtle biological signals. This study evaluates storage conditions, including two sample preparation methods and storage durations, to minimize systematic variability in the analysis of extracted lipids from latent fingerprints. In the traditional approach, samples are prepared immediately, stored as lipid extracts, and processed in multiple batches. In an alternative method, samples are stored directly on the deposition foil, and preparation is delayed until all can be processed in a single batch. Storage duration is evaluated to determine if shorter storage with analysis in multiple batches is more effective than longer storage with analysis in a single batch. Our findings demonstrate that storage of latent fingerprint samples on the deposition foil is a viable option, with minimal degradation of key features even after eight months of storage. While some differences in lipid profiles were observed across storage conditions, these differences were minor and would likely have little impact in larger studies where biological variability is greater. These insights offer practical guidance for implementing latent fingerprint sampling in large-scale studies by identifying optimal conditions that preserve sample quality and streamline workflows. Full article

A novel method for the development of fingerprints in environmentally friendlier conditions and on porous surfaces with 1,8-diazafluoren-9-one (DFO) is reported herein. DFO, a fluorescent dye was formulated in glacial acetic acid, methanol, and a minimum amount of methylene chloride. The DFO reacted with amino acid components of latent prints, resulting in a fluorescent species that was visualized under daylight, UV light at 254 nm, 365 nm, and LED at 395–405 nm. The developed prints were photographed using iPhone 11 and IOS 17.4.1. The fluorescent spectra of the species resulting from DFO’s reaction with the amino acid glycine and the wavelengths of maximum excitation (λ_ex = 470 nm) and emission (λ_em = 585 nm) were also reported. The method is suitable for forensic laboratories. Full article

The global demand for legumes has grown significantly since the 1960s, due to their high protein content and environmental benefits. However, this growth could also facilitate the spread of seed-borne pathogens like Curtobacterium flaccumfaciens pv. flaccumfaciens (Cff). Cff is a Gram-positive bacterium causing bacterial wilt in common beans and poses substantial challenges in regard to its detection and management, due to its long latent period and xylemic nature. Traditional diagnostic methods have proven insufficient, highlighting the need for innovative approaches. This study explores the potential of volatile organic compounds (VOCs) produced by Cff to be used as diagnostic markers to prevent the spread of seed-borne pathogens. First, we analyzed the VOCs emitted by different Cff strains in vitro, identifying a unique blend of five major VOCs. Subsequently, we verified the presence of these VOCs in vivo in artificially infected Cannellino beans. Phenylmethanol and 2-methoxy-4-vinylphenol emerged as key diagnostic markers, differentiating Cff from other bacterial pathogens of beans, such as Pseudomonas savastanoi pv. phaseolicola and Xanthomonas phaseoli pv. phaseoli. Our findings suggest that VOC fingerprinting offers a non-invasive, effective method for the early detection of Cff, even in asymptomatic seeds. This innovative approach holds significant promise for improving seed-borne disease management and supporting the development of practical diagnostic tools for field applications. Further research should aim to enhance the sensitivity and specificity of VOC-based diagnostics, facilitating the rapid and accurate screening of plant materials at ports of entry. This would contribute to the sustainability and health of leguminous crop production. Full article

Sebum lipids, accessible via groomed latent fingerprints, may be a valuable, underappreciated sample source for future biomarker research. Sampling sebum lipids from the skin is painless for patients, efficient for researchers, and has already demonstrated the potential to contain disease biomarkers. However, before sebum sampling can be implemented in routine studies, more information is needed regarding sampling reproducibility and variability. This information will enable researchers to choose the best practices for sebum-based studies. Herein, we use our recently established workflow for the collection and analysis of groomed fingerprints to assess the reproducibility of lipid profiles obtained via mass spectrometry. Using 180 fingerprint samples collected from 30 participants, we also assess lipid changes according to biological sex and anatomical grooming region (cheek, neck, and forehead) via supervised and unsupervised classification. The results demonstrate that this sampling protocol achieves satisfactory reproducibility, and negligible differences exist between male and female groomed fingerprint lipids. Moreover, the anatomical grooming region can impact the fingerprint lipid profile: cheek- and forehead-groomed fingerprints are more similar to one another than either collection site is to neck-groomed fingerprints. This information will inform future sebum-based biomarker investigations, enabling researchers to collect meaningful lipidomic datasets from groomed fingerprint samples. Full article

In this study, hybrid materials were synthesized incorporating curcumin, Cu²⁺ or Fe³⁺, and Kappa-carrageenan as a reducing agent to improve stability, considering that curcumin has low thermal and solution stability, which limits its applications. Colorimetric analysis showed color changes in the hybrids, ultraviolet–visible spectroscopy revealed band shifts in the hybrids, and infrared analysis indicated shifts in wavenumbers, suggesting changes in the vibrational state of curcumin after bonding with metal ions. These techniques confirmed the formation of hybrid materials. Thermogravimetric and chromatographic analyses demonstrated greater thermal and solution stability for the hybrids compared to curcumin. Additionally, the hybrid composites effectively developed natural and sebaceous latent fingerprints with good clarity and contrast on glass surfaces. Both composites performed similarly to commercial Gold^® powder. When applied to surfaces representative of forensic scenarios, the composites were versatile, revealing sufficient fingerprint details for human identification on both porous and non-porous surfaces. Scanning electron microscopy images showed greater clarity in sebaceous and natural fingerprints developed with the Fe composite compared to the Cu composite. Full article

In this study, we employed machine learning techniques to improve sustainable materials design by examining how various latent space representations affect the AI performance in property predictions. We compared three fingerprinting methodologies: (a) neural networks trained on specific properties, (b) encoder–decoder architectures, and c) traditional Morgan fingerprints. Their encoding quality was quantitatively compared by using these fingerprints as inputs for a simple regression model (Random Forest) to predict glass transition temperatures (Tg), a critical parameter in determining material performance. We found that the task-specific neural networks achieved the highest accuracy, with a mean absolute percentage error (MAPE) of 10% and an R² of 0.9, significantly outperforming encoder–decoder models (MAPE: 19%, R²: 0.76) and Morgan fingerprints (MAPE: 24%, R²: 0.6). In addition, we used dimensionality reduction techniques, such as principal component analysis (PCA) and t-distributed stochastic neighbour embedding (t-SNE), to gain insights on the models’ abilities to learn relevant molecular features to Tg. By offering a more profound understanding of how chemical structures influence AI-based property predictions, this approach enables the efficient identification of high-performing materials in applications that range from water decontamination to polymer recyclability with minimum experimental effort, promoting a circular economy in materials science. Full article

A group of bithiophenyl compounds comprising the cyanoacrylate moiety were designed and successfully synthesized. The optical, (spectro)electrochemical, and aggregation-induced emission properties were studied. DFT calculations were used to explain the reaction’s regioselectivity and to determine the molecules’ energy parameters (i.e., band gaps, HOMO levels, and LUMO levels). The aggregation-induced emission of compounds has been studied in the mixture of DMF (as a good solvent) and water (as a poor solvent), with different water fractions ranging from 0% to 99%. It has been shown that there are differences in the physicochemical properties of the obtained compounds due to the length of the alkyl chain in the ester group. Investigated derivatives were tested for their potential use in visualizing latent fingerprints and electrochromic materials. Full article

As a part of the important species that form coral reef ecosystems, stony corals have become a potential source of pharmacologically active lead compounds for an increasing number of compounds with novel chemical structures and strong biological activity. In this study, the secondary metabolites and biological activities are reported for Aspergillus terreus C21-1, an epiphytic fungus acquired from Porites pukoensis collected from Xuwen Coral Reef Nature Reserve, China. This strain was cultured in potato dextrose broth (PDB) media and rice media with different salinities based on the OSMAC strategy. The mycelial morphology and high-performance thin layer chromatographic (HPTLC) fingerprints of the fermentation extracts together with bioautography were recorded. Furthermore, an untargeted metabolomics study was performed using principal component analysis (PCA), orthogonal projection to latent structure discriminant analysis (O-PLSDA), and feature-based molecular networking (FBMN) to analyze their secondary metabolite variations. The comprehensive results revealed that the metabolite expression in A. terreus C21-1 differed significantly between liquid and solid media. The metabolites produced in liquid medium were more diverse but less numerous compared to those in solid medium. Meanwhile, the mycelial morphology underwent significant changes with increasing salinity under PDB cultivation conditions, especially in PDB with 10% salinity. Untargeted metabolomics revealed significant differences between PDB with 10% salinity and other media, as well as between liquid and solid media. FBMN analysis indicated that alkaloids, which might be produced under high salt stress, contributed largely to the differences. The biological activities results showed that six groups of crude extracts exhibited acetylcholinesterase (AChE) inhibitory activities, along with 1,1-diphenyl-2-picrylhydrazyl (DPPH) free radical scavenging and antibacterial activities. The results of this study showed that the increase in salinity favored the production of unique alkaloid compounds by A. terreus C21-1. Full article

Blue-emitting bismuth-doped lanthanum oxide (La₂O₃: Bi³⁺) with various concentrations of Bi was synthesized using the sol–gel combustion method and used for visualization of latent fingerprints (LFPs). An X-ray diffraction (XRD) study revealed the hexagonal structure of the phosphors and total incorporation of the bismuth in the La₂O₃ matrix. Field Emission Scanning Electron Microscopy (FE-SEM) and Fourier Transform Infrared Spectroscopy (FTIR) were used to study the morphology and the relative vibrations of the synthesized samples. Photoluminescence (PL) studies showed strong blue emission around 460 nm due to the ³P₁ → ¹S₀ transition. Clear bright-blue fingerprint images were obtained with the powder dusting method on various surfaces like aluminum, compact discs, glass, wood and marble. A first evaluation of these images indicated a clear visualization of all three levels of details and a very high contrast ranging from 0.41 on marble to 0.90 on aluminum. As a further step, we used an algorithm for extracting fingerprint minutiae with which we succeeded in detecting all three levels of fingerprint details and even the most difficult ones, like open and closed pores. According to these analyses, La₂O₃: Bi phosphor is demonstrated to be an effective blue fluorescent powder for excellent visualization of latent fingerprints. Full article

A novel method for synthesizing dumbbell-shaped (Gd_1−xTb_x)₂O(CO₃)₂·H₂O (GOC:xTb³⁺) phosphors using sodium carbonate was investigated. An amount of 1 mmol of stable fluorescent powder can be widely prepared using 3–11 mmol of Na₂CO₃ at a pH value of 8.5–10.5 in the reaction solution. The optimal reaction conditions for the phosphors were determined to be 7 mmol for the amount of sodium carbonate and a pH of 9.5 in the solution. Mapping analysis of the elements confirmed uniform distribution of Gd³⁺ and Tb³⁺ elements in GOC:xTb³⁺. The analysis of fluorescence intensity shows that an optimal excitation wavelength of 273 nm is observed when the concentration of Tb³⁺ is between 0.005 and 0.3. The highest emission intensity was observed for GOC:0.05Tb³⁺ with a 57.5% maximum quantum efficiency. The chromaticity coordinates show that the color of GOC:Tb³⁺ is stable and suitable for fluorescence recognition. Latent fingerprint visualization reveals distinctive features like whorls, hooks, and bifurcations. Therefore, the sodium carbonate method offers an effective alternative to traditional urea chemical reaction conditions for preparing GOC:Tb³⁺. Full article

Automated canopy stress classification for field crops has traditionally relied on single-perspective, two-dimensional (2D) photographs, usually obtained through top-view imaging using unmanned aerial vehicles (UAVs). However, this approach may fail to capture the full extent of plant stress symptoms, which can manifest throughout the canopy. Recent advancements in LiDAR technologies have enabled the acquisition of high-resolution 3D point cloud data for the entire canopy, offering new possibilities for more accurate plant stress identification and rating. This study explores the potential of leveraging 3D point cloud data for improved plant stress assessment. We utilized a dataset of RGB 3D point clouds of 700 soybean plants from a diversity panel exposed to iron deficiency chlorosis (IDC) stress. From this unique set of 700 canopies exhibiting varying levels of IDC, we extracted several representations, including (a) handcrafted IDC symptom-specific features, (b) canopy fingerprints, and (c) latent feature-based features. Subsequently, we trained several classification models to predict plant stress severity using these representations. We exhaustively investigated several stress representations and model combinations for the 3-D data. We also compared the performance of these classification models against similar models that are only trained using the associated top-view 2D RGB image for each plant. Among the feature-model combinations tested, the 3D canopy fingerprint features trained with a support vector machine yielded the best performance, achieving higher classification accuracy than the best-performing model based on 2D data built using convolutional neural networks. Our findings demonstrate the utility of color canopy fingerprinting and underscore the importance of considering 3D data to assess plant stress in agricultural applications. Full article

Fluorescent carbon dots (CDs) are a new type of photoluminescent nanomaterial. Solid-state CDs usually undergo fluorescence quenching due to direct π-π* interactions and superabundant energy resonance transfer. Therefore, the preparation of solid-state fluorescent CDs is a challenge, especially the preparation of long wavelength solid-state CDs. In this research, long wavelength emission CDs were successfully synthesized by solvothermal methods, and the prepared CDs showed good hydrophobicity. The composite solid-state CDs/PVP (Polyvinyl pyrrolidone) can emit strong red fluorescence, and the quantum yield (QY) of the CDs/PVP powder reaches 18.9%. The prepared CDs/PVP solid-state powder was successfully applied to latent fingerprint detection. The results indicate that the latent fingerprints developed by CDs/PVP powder have a fine definition and high contrast visualization effect, which proves that the prepared CDs/PVP has great application potential in latent fingerprint detection. This study may provide inspiration and ideas for the design of new hydrophobic CDs. Full article