Search Results (284)

Modern Greek was spoken along the northwestern coast of Asia Minor until the early 20th century, yet neither its precise geographical extent nor its dialectal classification is well established. This paper seeks to clarify both issues by focusing on the variety of Adramytti (Edremit). The available evidence suggests that Adramyttian, despite its close relationship to and partial origin in Modern Lesbian, was essentially a mixed variety that leveled out many characteristic Modern Lesbian features, such as the raising of unstressed mid vowels and certain morphological phenomena. Such differences can be attributed to the diverse character of the speech community that led to contact between speakers of Modern Lesbian origin and speakers of other Greek dialects. In addition to providing a grammatical description of Adramyttian, which demonstrates its mixed profile, the paper offers a tentative classification of this variety in relation to Modern Lesbian and the other insular varieties of northeastern Aegean, as well as in relation to other neighboring varieties of northwestern Asia Minor (Aeolis, Mysia, northern Ionia). Full article

Pyropia haitanensis, an economically significant cultivated seaweed in China, exhibits substantial geographical variations in nutritional and sensory qualities that influence its market value. The nutritional quality of the samples, including total sugar, total protein, and amino acid content, as well as color quality, assessed through phycobiliprotein and chlorophyll content, and sensory quality evaluated using an electronic nose and electronic tongue, were determined. To elucidate these quality variations, this study employed an integrated metabolomics and chemometrics approach to analyze samples from five major cultivation regions. Principal component analysis (PCA) effectively differentiated the samples; orthogonal partial least squares discriminant analysis (OPLS-DA) validated this classification with robust model parameters (R²X = 0.791, R²Y = 0.995, Q² = 0.984) and identified key discriminatory metabolites. Weighted gene co-expression network analysis (WGCNA) identified origin-specific metabolic modules correlated with quality traits, revealing that pathways such as cysteine and methionine metabolism underpin the observed differences in flavor profiles across cultivation regions. Furthermore, mediation analysis quantitatively confirmed that inorganic nitrogen primarily influences key flavor attributes by regulating sulfur-containing amino acid and nucleotide metabolism. This study systematically elucidates the metabolic mechanisms governing quality formation in P. haitanensis, providing a scientific foundation for quality control and geographical origin traceability. Full article

Accurate monitoring of crop phenology is essential for precision agriculture and yield forecasting. However, satellite-derived time series often suffer from inherent noise, such as residual atmospheric effects and mixed pixels, as well as a frequent lack of ground-truth data in agriculture. In response, this study proposes an algorithm to define the type of spectral signatures for the principal phenological stages of crops, using them as the foundation for training supervised machine learning classification models. The algorithm was developed using Fuzzy C-Means (FCM) clustering to identify the spectral signature reference groups in winter wheat across the Burgos region (Spain) during the 2020 and 2021 growing seasons. To enhance cluster independence and biological coherence, a multi-step filtering process was implemented, including spectral purity (membership degree, SAM, and SAMder) and temporal coherence filters. The filtered and labeled dataset (80% original Burgos dataset) was used to train supervised classification models (KNN and XGBoost). The models’ reliability was verified through three wheat tests (remaining 20%), labeled using other clustering techniques, and an independent barley dataset from diverse geographic locations (Valladolid and Soria). The filtering process significantly improved cluster stability by removing outliers and transition spectral signatures. The supervised models demonstrated exceptional performance; the KNN model slightly outperformed XGB, achieving a mean Accuracy of 0.977, a Kappa of 0.967, and an F1-score of 0.977 in the wheat external test. Furthermore, the model showed, when applied to barley, that its phenological spectral signatures are equivalent in shape to those of wheat, with an Accuracy of 0.965 and an F1-score of 0.974. In addition, it was verified that the type spectral signatures remain the same regardless of the location. This study presents a robust classification tool capable of labeling four key phenological stages (tillering, stem elongation, ripening, and senescence) without ground truth. By effectively removing inherent satellite noise, the proposed methodology produces organized, cleaned datasets. This structured foundation is critical for future research integrating spectral signatures with harvester data to develop high-precision yield prediction models. Full article

The research creates classification maps of wind turbine operational speeds based on the wind regimes of four governorates in central and southern Iraq: Wasit, Diwaniyah, Maysan, and Dhiqar. High-resolution wind data from GEOSUN resource maps, together with statistical analysis of the Weibull distribution, are used to derive site-specific shape and scale parameters, which are then utilized to calculate the ideal cut-in, rated, and cut-out wind speeds for each location. A turbine performance index integrates capacity factor and normalized power output to determine the turbine speed combination that optimizes energy production for the local wind distribution. The resultant maps exhibit distinct geographical gradients: in all four governorates, cut-in, rated, and cut-out speeds consistently escalate towards the eastern regions of the research area, therefore broadening the range of technologically suitable turbines. Quantitatively, Wasit demonstrates the highest rated wind speeds, ranging from approximately 11.1 to 14.9 m per second, and cut-out speeds from about 20.5 to 27.6 m per second, indicating superior wind resource quality relative to other governorates. In contrast, Diwaniyah is suitable for lower-speed turbines, with minimum rated speeds between 8.9 and 9.5 m per second and minimum cut-out speeds around 16.6 to 17.6 m per second. Analysis of wind direction indicates that around fifty percent of the wind power potential originates from the northwest sector, suggesting that turbines should be aligned toward the northwest to optimize yearly energy acquisition. The maps serve as an effective decision support instrument that connects quantitative wind resource assessment to turbine operational specifications, facilitating expedited preliminary turbine selection, enhanced energy efficiency, and diminished dependence on traditional fossil fuel power plants in areas experiencing persistent electricity deficits. Full article

Traditional sensory evaluation of traditional Chinese medicine (TCM) and medicinal and food homologous products has long relied on human observation of appearance, color, aroma, and taste. However, this approach is highly subjective, difficult to quantify, and often lacks reproducibility across evaluators. Intelligent sensory systems, including the electronic nose, electronic tongue, and machine vision, provide objective and digitized sensory information for TCM quality evaluation. Nevertheless, these platforms generate high-dimensional and heterogeneous datasets, creating a strong demand for efficient artificial intelligence (AI)-based analytical tools. This review summarizes recent advances in the application of machine learning and deep learning methods, such as support vector machine, random forest, convolutional neural network, and long short-term memory networks, for intelligent sensory evaluation of TCM. Particular emphasis is placed on how AI supports feature extraction, pattern recognition, classification, regression, and multisource data fusion across electronic nose, electronic tongue, and machine vision systems. Representative applications in raw material authentication, geographical origin discrimination, processing monitoring, and quality grading are also discussed. In addition, the current challenges related to data standardization, sensor drift, model robustness, and interpretability are highlighted. Overall, this review provides an integrated overview of AI-enabled intelligent sensory technologies and clarifies their potential to advance TCM quality evaluation toward a more objective, efficient, and holistic framework. Full article

Accurately tracing the geographical origin of Zanthoxylum schinifolium Sieb. et Zucc. is important for brand authentication, quality control, and food safety assurance. In this study, the stable isotope ratios (δ¹³C, δ¹⁵N, δ²H, δ¹⁸O) and the contents of 20 elements were analyzed in samples from three major production regions. Significant differences (p < 0.05) were observed in δ¹³C, δ²H, δ¹⁸O and most elemental profiles across origins. Chemometric methods—including principal component analysis (PCA), orthogonal partial least squares-discriminant analysis (OPLS-DA), and linear discriminant analysis (LDA)—were applied to classify samples by geographical origin. OPLS-DA identified key discriminators (VIP > 1) such as Ca, δ¹³C, Mg, δ²H, B, δ¹⁸O, Cr, Ni, Na, Pb, As, Co, Se, and Zn, achieving a classification accuracy of 96.8%. LDA based on the combined isotope and element datasets showed even higher performance, with an original discrimination rate of 98.4% and a cross-validated rate of 92.8%. The results demonstrate that integrating stable isotope and multi-element fingerprints with supervised classification models provides a reliable and effective approach for verifying the geographical origin of Zanthoxylum schinifolium, supporting its use in traceability systems and fair trade practices. Full article

Background/Objectives: Accessory mandibular foramina (AMaFs) are small osseous openings of the mandible that are clinically relevant anatomical variations. This study aimed to characterize the morphology and spatial distribution of AMaFs in dry mandibles and to integrate the existing anatomical evidence through a systematic review and meta-analysis, with the goal of clarifying their potential clinical relevance. Methods: A series of dry mandibles from human adults of unknown age and sex from our laboratory collection was examined to document AMaFs using direct osteological inspection. Stainless steel wire threads and digimatic caliper measurements were utilized by two separate raters. Cluster analysis was employed for the classification of foramina into distinct spatial groups. Furthermore, in accordance with the PRISMA guidelines, an unrestricted literature search was conducted across PubMed, Scopus, SciELO, and Google Scholar using appropriate database-specific combinations of the terms “accessory mandibular” and “foramen/foramina” to search for studies on the prevalence and morphology of AMaFs in dry mandibles or cadaveric material. Radiological studies were excluded. The search was completed on 13 July 2025. Study quality was evaluated using the appropriate AQUA tool. Data synthesis was carried out using STATA 19. No external funding was received. Results: A total of 96 dry mandibles (50 dentate and 46 edentulous) were analyzed. AMaFs were detected in 8/96 mandibles (8.3%). In these mandibles, a total of 25 accessory mandibular foramina, all superior to the mandibular foramen, were identified (mean: 3.13 foramina/mandible), with a mean diameter (SD) of 0.56 ± 0.10 mm and a mean distance from the mandibular foramen of 11.34 ± 1.29 mm (mean vertical distance: 10.32 ± 1.35 mm; mean absolute horizontal distance: 3.78 ± 0.49 mm). Of these foramina, 21/25 (84%) had a diameter ≥0.5 mm; the number, diameters, and distances from the mandibular foramen were comparable between left and right hemimandibles. Based on their positioning relative to the mandibular foramen, the AMaFs were classified into two distinct groups (clusters). In the meta-analysis, a total of 36 studies were included. In most of the mandibles (65.1%; 95% CI: 57.7–72.2%; I²: 94.9%), no AMaFs were detected. The unilateral presence of one or more AMaFs was observed in 20.9% of the mandibles (95% CI: 16.3–25.9%; I²: 91.3%), while bilateral occurrence was identified in 10.6% (95% CI: 6.9–15.0%; I²: 93.0%). Additionally, 2.4% of the mandibles (95% CI: 1.0–4.2%; I²: 86.3%) exhibited multiple AMaFs (≥2) on at least one side. On average, each hemimandible contained 0.253 AMaFs (95% CI: 0.198–0.312; I²: 96.9%). The overall mean diameter of AMaFs was estimated to be 0.65 ± 0.33 mm. The substantial heterogeneity observed was not explained by geographic origin, sample size, publication period, or publication bias. Conclusions: AMaFs were detected in approximately one-third of the mandibles in the studies included in the meta-analysis. AMaFs are typically located superior to the mandibular foramen and may represent additional anatomical pathways associated with inferior alveolar nerve branching. Awareness of these features could help clinicians to anticipate anatomical variability during mandibular surgery and when applying local anesthesia. In addition, it should be acknowledged that inferior alveolar nerve block failure is multifactorial and not solely determined by the presence of AMaFs. Full article

Anthurium (Araceae) is one of the most species-rich Neotropical genera, yet its infrageneric classification remains unresolved. This study tests the monophyly of the morphologically defined Anthurium sect. Pachyneurium diagnosed by rosulate habit, involute prefoliation, and absence of a collective vein with a focus on Brazilian species. Using target capture sequencing (Angiosperms353 probe set), we generated a phylogenomic dataset for 35 Anthurium species (18 from sect. Pachyneurium) and conducted maximum likelihood and coalescent-based analyses. Our results demonstrate that sect. Pachyneurium is not monophyletic as traditionally circumscribed. Brazilian species previously assigned to the section are recovered in three geographically structured and strongly supported lineages: Amazonian, Atlantic Forest, and Caatinga/Cerrado. The Atlantic Forest lineage is unexpectedly resolved as sister to A. coriaceum (sect. Urospadix), revealing an evolutionary relationship not predicted by morphology. Divergence-time estimates place the origin of crown Anthurium in the Paleocene (~62 Ma), with diversification of the Brazilian lineages occurring during the Miocene (20–3 Ma), coinciding with major geoclimatic events in South America. Our findings indicate that key diagnostic morphological characters are homoplastic and provide a phylogenomic framework for revising the infrageneric classification of Anthurium. By identifying evolutionarily distinct lineages, this study also contributes to prioritizing conservation efforts in threatened Neotropical biomes. Full article

With evolving consumption trends, the coffee market is experiencing increasing demand for high-quality, traceable coffees, which, in turn, has led to price growth. Therefore, due to its increased economic value, coffee has become a constant target of fraudulent actions. As result, many analytical techniques have been explored as tools for coffee classification and authentication, of which the use of digital, hyperspectral and/or multispectral imaging is noteworthy. This type of analysis provides rapid, non-destructive, environmentally friendly, and increasingly accessible alternatives to conventional analytical methods. By consulting three different databases, this work systematically revised articles published in the last 10 years, which utilize digital image analysis and hyper/multispectral imaging for the botanical and geographical classification and authentication of coffees. The reviewed studies (n = 17) demonstrate that, when paired with classification algorithms, discrimination across species, origins, and quality categories can be achieved. A critical point to highlight is the importance of using whole beans and standardizes roast degree to avoid biasing the models. Concerning digital images, relying solely on color features limits the robustness of the classification models. Incorporating complementary textural and shape features is thus necessary to capture the coffee botanical or geographic information, as shown in a minor number of the selected studies. In a similar fashion, for hyper/multispectral imaging, there is still potential to further exploit the spatial information, thus achieving the technique’s full potential. The evidence indicates that image-based methods are steadily progressing into reliable tools for coffee authentication. Full article

The management of specialty coffee production represents a complex dynamical process characterized by highly nonlinear interconnections between environmental variables, agronomic practices, and chemical compositions. Traditionally, the classification of specialty coffee relies on sensory evaluations conducted by highly certified coffee experts named Q-Graders, using a strict, standardized Specialty Coffee Association (SCA) protocol. However, scientific methods that generate spectral fingerprints provide a more reliable guarantee of quality while also ensuring traceability to the farm of origin. Panamanian Geisha coffee is one of the world’s most expensive award-winning microlots, frequently exceeding 1000 American dollars per pound, with a record-breaking price of over 30,000 American dollars per kilogram in 2025. This research presents an integrated framework that combines Precision Agriculture Management Systems (PAMSs) and a traceability architecture that facilitates the collection of georeferenced coffee bean samples using a mobile application (apps), while preserving the coffee varieties and geographical origin necessary for the subsequent identification of the spectral fingerprint by chemical specialists in their laboratory. A mathematical model is introduced to formally characterize the mobile application’s behavior, distributed structure, and inherent constraints. Serving as a mathematical blueprint, this model identifies critical influencing factors and establishes strategic assumptions to distill complex real-world variables into a rigorous, manageable framework. Large-scale experiments conducted across more than 820 coffee farms in Chiriquí, Panama, demonstrate that the proposed decentralized architecture effectively coordinates the acquisition and synchronization of georeferenced chemical data. The decentralized architecture of the mobile application utilizes private blockchain technology to facilitate autonomous operations, effectively decoupling the system from central authorities to ensure functional continuity in environments characterized by intermittent connectivity. Full article

The European sardine Sardina pilchardus, a key marine resource in Portugal and Spain, experienced severe population declines in the 2000s. To support its recovery, confirming the geographic origin of European sardine is essential. This study examines the spatial and temporal variability of elemental fingerprints (EF), using Inductively Coupled Plasma Mass Spectrometry (ICP-MS), of S. pilchardus scales. Specimens were collected from seven (in 2018) and five (in 2019) fishing harbors in Galicia (Spain) and mainland Portugal to confirm their location and time of capture, as well as evaluate how temporal variability influences the location predictive models when samples from different years are used for model development and testing. Thirteen elements (Ba, Ca, Co, Cr, K, Mg, Mn, Na, Ni, P, Sr, V, and Zn) were used in the models developed. Random Forest models using samples from 2018 and 2019 correctly classified over 95% of the specimens by location, within each year. Capture time classification achieved 95.3% accuracy. However, applying the 2018 model to samples from 2019 reduced accuracy to only 24.4%. Despite this constraint, the EF of fish scales provide a practical and reliable method to confirm capture time and geographic origin, allowing a more sustainable management of S. pilchardus stocks. Full article

Owing to the high pharmacological relevance and multidimensional quality attributes of Panax spp., accurate authentication and quality evaluation of Panax-derived herbal materials remain challenging within traditional Chinese medicine (TCM) quality control systems. Conventional approaches often face trade-offs among analysis speed and throughput, non-destructive measurement, and analytical accuracy, which can limit their suitability for modern, large-scale quality control. This review summarizes recent advances in vibrational and related analytical techniques—infrared (IR) and near-infrared (NIR) spectroscopy, Raman spectroscopy, terahertz (THz) spectroscopy, hyperspectral imaging (HSI), and nuclear magnetic resonance (NMR)—for authentication and quality evaluation of Panax materials. We compare the capabilities of each modality in supporting key tasks, including species authentication, geographical origin tracing, age/cultivation-stage discrimination, and quantitative assessment of major chemical markers, with emphasis on the underlying measurement principles. In general, NIR and HSI are well suited to rapid, high-throughput screening of bulk samples, whereas Raman and NMR provide higher chemical specificity for molecular and structural characterization. To mitigate limitations of single-modality analysis, this review discusses a methodological shift from conventional spectral fingerprinting and chemometric approaches toward model-driven, data-enabled sensing strategies for robust quality evaluation. Specifically, we highlight multimodal data fusion frameworks combined with interpretable machine-learning/deep-learning methods to build robust classification and regression models for quality assessment. This perspective aims to support standardized and scalable authentication and quality evaluation of Panax herbal materials and to facilitate the digitization of quality control workflows for Chinese herbal medicines. Full article

This paper publishes 735,000 historical passenger entries from the German North Sea port of Bremen, created between 1830 and 1939, and now structured, enriched, and processed into a research-ready database. It provides an overview of the original archival documents and their datafication, beginning with a historical account of why the passenger lists were created and which information they recorded. Building on extensive prior work—largely carried out by a team of volunteer transcribers with expertise in family history and genealogy—the lists were transcribed manually and first made available online in 2003. To enhance their analytical value, we computationally post-processed these data through (1) data cleaning, especially addressing spelling variants and transcription errors; (2) data normalisation, including conversion into standardised formats; and (3) data augmentation by adding identifiers, geographic information, and multiple classifications. Finally, we discuss limitations of the resulting dataset as well as its analytical potential. Full article

Against the dual backdrop of the rural revitalization strategy and the pursuit of high-quality, balanced urban–rural education, optimizing rural campus spaces has emerged as an important lever for addressing educational resource disparities and improving pedagogical quality. However, conventional evaluation of campus space optimization faces two systemic dilemmas. First, top-down decision-making often neglects the authentic needs of diverse stakeholders and place-based knowledge, resulting in spatial interventions that lose regional distinctiveness. Second, routine public participation is constrained by geographical barriers, time costs, and sample-size limitations, which can amplify professional cognitive bias and impede comprehensive feedback formation. The compounded effect of these challenges contributes to a disconnect between spatial optimization outcomes and perceived needs, thereby constraining the distinctive development of rural educational spaces. To address these constraints, this study proposes a novel method that integrates regional spatial feature recognition with digital media-based public perception assessment. At the data collection and ethical governance level, the study strictly adheres to platform compliance and academic ethics. A total of 12,800 preliminary comments were scraped from major social media platforms (e.g., Douyin, Dianping, and Xiaohongshu) and processed through a three-stage screening workflow—keyword screening–rule-based filtering–manual verification—to yield 8616 valid records covering diverse public groups across China. All user-identifying information was fully anonymized to ensure lawful use and privacy protection. At the analytical modeling level, we develop a Transformer-based deep learning system that leverages multi-head attention mechanisms to capture implicit spatial-sentiment features and metaphorical expressions embedded in review texts. Evaluation on an independent test set indicates a classification accuracy of 89.2%, aligning with balanced and stable scoring performance. Robustness is further strengthened by introducing an equal-weight alternative strategy and conducting stability checks to indicate the consistency of model outputs across weighting assumptions. At the scenario interpretation level, we combine grounded-theory coding with semantic network analysis to establish a three-tier spatial analysis framework—macro (landscape pattern/hydro-topological patterns), meso (architectural interface), and micro (teaching scenes/pedagogical scenarios)—and incorporate an interpretive stakeholder typology (tourists, residents, parents, and professional groups) to systematically identify and quantify key features shaping public spatial perception. Findings show that, at the macro level, naturally integrated scenarios—such as “campus–farmland integration” and “mountain–water embeddedness”—exhibit high affective association, aligning with the “mountain-water-field-village” spatial sequence logic and suggesting broad public endorsement of ecological campus concepts, whereas vernacular settlement-pattern scenarios receive relatively low attention due to cognitive discontinuities. At the meso level, innovative corridor strategies (e.g., framed vistas and expanded corridor spaces) strengthen the building–nature interaction and suggest latent value in stimulating exploratory spatial experience. At the micro level, place-based practice-oriented teaching scenes (e.g., intangible cultural heritage handcraft and creative workshops) achieve higher scores, aligning with the compatibility of vernacular education’s “differential esthetics,” while urban convergence-oriented interdisciplinary curriculum scenes suggest an interpretive gap relative to public expectations. These results indicate an embedded relationship between public perception and regional spatial features, which is further shaped by a multi-actor governance process—characterized by “Government + Influencers + Field Study”—that mediates how rural educational spaces are produced, communicated, and interpreted in digital environments. The study’s innovative value lies in integrating sociological theories (e.g., embeddedness) with deep learning techniques to fill the regional and multi-actor perspective gap in rural campus POE and to promote a methodological shift from “experience-based induction” toward a “data-theory” dual-drive model. The findings provide inferential evidence for rural campus renewal and optimization; the methodological pipeline is transferable to small-scale rural primary schools with media exposure and salient regional ecological characteristics, and it offers a new pathway for incorporating digital media-driven public perception feedback into planning and design practice. The research methodology of this study consists of four sequential stages, which are implemented in a systematic and progressive manner: First, data collection was conducted: Python and the Octopus Collector were used to crawl online comment data related to Fuwen Township Central Primary School, strictly complying with the user agreements of the Douyin, Dianping, and Xiaohongshu platforms. Second, semantic preprocessing was performed: The evaluation content was segmented to generate word frequency statistics and semantic networks; qualitative analysis was conducted using Origin software, and quantitative translation was realized via Sankey diagrams. Third, spatial scene coding was carried out: Combined with a spatial characteristic identification system, a macro–meso–micro three-tier classification system for spatial scene characteristics was constructed to encode and quantitatively express the textual content. Finally, sentiment quantification and correlation analysis was implemented: A deep learning model based on the Transformer framework was employed to perform sentiment quantification scoring for each comment; Sankey diagrams were used to quantitatively correlate spatial scenes with sentiment tendencies, thereby exploring the public’s perceptual associations with the architectural spatial environment of rural campuses. Full article

Licorice (Glycyrrhiza uralensis Fisch.) is a widely used natural sweetener and functional food ingredient. Its sensory profile, nutritional value, and bioactive composition are strongly affected by geographical origin and cultivation mode, particularly the distinction between wild and cultivated resources. Consequently, developing a rapid and robust method for origin traceability is imperative for rigorous quality control and product standardization. This study proposes a non-destructive traceability framework integrating near-infrared (NIR) spectroscopy with a Support Vector Machine (SVM). The method’s validity was rigorously evaluated using a comprehensive dataset collected from China’s three primary production regions—Gansu Province, the Inner Mongolia Autonomous Region, and the Xinjiang Uygur Autonomous Region, encompassing both wild and cultivated resources. Experimental results demonstrated that the proposed framework achieved an overall classification accuracy exceeding 99%. The results show that the proposed method offers a rapid, efficient, and environmentally friendly analytical tool for the quality assessment of licorice, providing a scientific basis for rigorous quality control and standardization in the functional food industry. Full article