Search Results (1,822)

This article traces the historical evolution of mural painting as a medium of cultural expression from prehistoric cave art to contemporary street interventions. Adopting a diachronic and interdisciplinary approach, it investigates how muralism has developed across civilizations in relation to techniques, symbolic systems, social function, and its embeddedness in architectural and urban contexts. The analysis is structured around key historical periods using emblematic case studies to examine the interplay between materiality, iconography, and socio-political meaning. From sacred enclosures and civic monuments to post-industrial walls and digital projections, murals reflect shifting cultural paradigms and spatial dynamics. This study emphasizes how mural painting, once integrated into sacred and imperial architecture, has become a tool for public participation, protests, and urban storytelling. Particular attention is paid to the evolving relationship between wall painting and the spaces it inhabits, highlighting the transition from permanence to ephemerality and from monumentality to immediacy. This article contributes to mural studies by offering a comprehensive framework for understanding the technical and symbolic transformations of the medium while proposing new directions for research in the context of digital urbanism and cultural memory. Full article

The global trend toward Industry 4.0 has intensified the demand for intelligent, adaptive, and energy-efficient manufacturing systems. Machine learning (ML) has emerged as a crucial enabler of this transformation, particularly in high-mix, high-precision environments. This review examines the integration of machine learning techniques, such as convolutional neural networks (CNNs), reinforcement learning (RL), and federated learning (FL), within Taiwan’s advanced manufacturing sectors, including semiconductor fabrication, smart assembly, and industrial energy optimization. The present study draws on patent data and industrial case studies from leading firms, such as TSMC, Foxconn, and Delta Electronics, to trace the evolution from classical optimization to hybrid, data-driven frameworks. A critical analysis of key challenges is provided, including data heterogeneity, limited model interpretability, and integration with legacy systems. A comprehensive framework is proposed to address these issues, incorporating data-centric learning, explainable artificial intelligence (XAI), and cyber–physical architectures. These components align with industrial standards, including the Reference Architecture Model Industrie 4.0 (RAMI 4.0) and the Industrial Internet Reference Architecture (IIRA). The paper concludes by outlining prospective research directions, with a focus on cross-factory learning, causal inference, and scalable industrial AI deployment. This work provides an in-depth examination of the potential of machine learning to transform manufacturing into a more transparent, resilient, and responsive ecosystem. Additionally, this review highlights Taiwan’s distinctive position in the global high-tech manufacturing landscape and provides an in-depth analysis of patent trends from 2015 to 2025. Notably, this study adopts a patent-centered perspective to capture practical innovation trends and technological maturity specific to Taiwan’s globally competitive high-tech sector. Full article

The consumption of fresh fruit and vegetables is essential for a healthy diet as they contain a diverse composition of vitamins, minerals, fibre, and bioactive compounds. However, cross-contamination during harvest and post-harvest poses a high risk of microbial contamination. Therefore, handling fruit and vegetables during processing and contact with wet equipment and utensil surfaces is an ideal environment for microbial contamination and foodborne illness. Nevertheless, less attention has been paid to some emerging pathogens that are now increasingly recognised as transmissible to humans through contaminated fruit and vegetables, such as Arcobacter and Cronobacter species in various products, which are the main risk in fruit and vegetables. Cronobacter and Arcobacter spp. are recognised food-safety hazards because they pose a risk of foodborne disease, especially in vulnerable groups such as newborns and immunocompromised individuals. Cronobacter spp. have been linked to severe infant conditions—notably meningitis and sepsis—most often traced to contaminated powdered infant formula. Although Arcobacter spp. have been less extensively studied, they have also been associated with foodborne disease, chiefly from dairy products and meat. With this in mind, this review provides an overview of the main chemical and physical sanitisation methods in terms of their ability to reduce the contamination of fresh fruit and vegetable products caused by two emerging pathogens: Arcobacter and Cronobacter. Emerging chemical (organic acid compounds, extracts, and essential oils) and physical methods (combination of UV-C with electrolysed water, ultrasound, and cold atmospheric plasma) offer innovative and environmentally friendly alternatives to traditional approaches. These methods often utilise natural materials, less toxic solvents, and novel techniques, resulting in more sustainable processes compared with traditional methods that may use harsh chemicals and environmentally harmful processes. This review provides the fruit and vegetable industry with a general overview of possible decontamination alternatives to develop optimal and efficient processes that ensure food safety. Full article

The rapid neutron-capture process (r-process) is responsible for the creation of roughly half of the elements heavier than iron, including precious metals like silver, gold, and platinum, as well as radioactive elements such as thorium and uranium. Despite its importance, the nature of the astrophysical sites where the r-process occurs, and the detailed mechanisms of its formation, remain elusive. The key to resolving these mysteries lies in the study of chemical signatures preserved in ancient, metal-poor stars. These stars, which formed in the early Universe, retain the chemical fingerprints of early nucleosynthetic events and offer a unique opportunity to trace the origins of r-process elements in the early Galaxy. In this review, we explore the state-of-the-art understanding of r-process nucleosynthesis, focusing on the sites, progenitors, and formation mechanisms. We discuss the role of potential astrophysical sites such as neutron star mergers, core-collapse supernovae, magneto-rotational supernovae, and collapsars, that can play a key role in producing the heavy elements. We also highlight the importance of studying these signatures through high-resolution spectroscopic surveys, stellar archaeology, and multi-messenger astronomy. Recent advancements, such as the gravitational wave event GW170817 and detection of the r-process in the ejecta of its associated kilonovae, have established neutron star mergers as one of the confirmed sites. However, questions remain regarding whether they are the only sites that could have contributed in early epochs or if additional sources are needed to explain the signatures of r-process found in the oldest stars. Additionally, there are strong indications pointing towards additional sources of r-process-rich nuclei in the context of Galactic evolutionary timescales. These are several of the outstanding questions that led to the formation of collaborative efforts such as the R-Process Alliance, which aims to consolidate observational data, modeling techniques, and theoretical frameworks to derive better constraints on deciphering the astrophysical sites and timescales of r-process enrichment in the Galaxy. This review summarizes what has been learned so far, the challenges that remain, and the exciting prospects for future discoveries. The increasing synergy between observational facilities, computational models, and large-scale surveys is poised to transform our understanding of r-process nucleosynthesis in the coming years. Full article

In this work, a sub-modeling technique is proposed for the analysis of large-scale masonry structures. The approach couples an anisotropic macroscale formulation, derived by incorporating the notion of a fabric tensor for an orthotropic material, with mesoscale analysis. The latter employs distinct inelastic constitutive relations assigned to the brick material and brick-mortar interfaces, which enable the tracing of localized damage propagation. The mechanical properties at the macro-level are identified from the ‘virtual’ set of data generated through mesoscale analysis, ensuring consistency between the two approaches in representing the masonry material across different scales. In the numerical analysis, the macroscale approach is first applied over the entire domain to interpolate the kinematic boundary conditions in a local region of interest, which is then re-analyzed based on the mesoscale framework. The developed strategy is illustrated by simulating the shear response of a large-scale unreinforced masonry wall with multiple window openings. Full article

Traditional relaxation techniques such as meditation and slow breathing often rely on subjective self-assessment, making it difficult to objectively monitor physiological changes. Electrocardiograms (ECG), which are commonly used by clinicians, provide one-dimensional signals to interpret cardiovascular activity. In this study, we introduce a visual interpretation framework that transforms heart rate variability (HRV) time series into fuzzy recurrence plots (FRPs). Unlike ECGs’ linear traces, FRPs are two-dimensional images that reveal distinctive textural patterns corresponding to autonomic changes. These visually rich patterns make it easier for even non-experts with minimal training to track changes in relaxation states. To enable automated detection, we propose a multi-domain feature fusion framework suitable for wearable systems. HRV data were collected from 60 participants during spontaneous and slow-paced breathing sessions. Features were extracted from five domains: time, frequency, non-linear, geometric, and image-based. Feature selection was performed using the Fisher discriminant ratio, correlation filtering, and greedy search. Among six evaluated classifiers, support vector machine (SVM) achieved the highest performance, with 96.6% accuracy and 100% specificity using only three selected features. Our approach offers both human-interpretable visual feedback through FRP and accurate automated detection, making it highly promising for objectively monitoring real-time stress and developing biofeedback systems in wearable devices. Full article

This review presents an in-depth analysis of the synergistic role of molecular biology in advancing forensic entomology. The study discusses how insects associated with decomposing bodies provide critical data for estimating the post-mortem interval (PMI), and how molecular techniques improve species identification and trace analysis. The manuscript examines DNA-based methods such as RAPD, RFLP, and mitochondrial sequencing, along with innovative applications like gene expression profiling and entomotoxicology analysis. Additionally, it presents real case studies illustrating how molecular data from insects can be used not only to estimate PMI but also to identify victims or suspects through human DNA retrieved from insect tissues. These advances confirm the fundamental role of molecular biology in strengthening the reliability and applicability of forensic entomology in legal contexts. Full article

(1) Background: Elemental imaging methods such as XRF, SEM/TEM-EDS, LIBS and LA-ICP-MS are widely used in clinical diagnostics. Based on the results obtained, it is possible to assess the safety of both standard and innovative therapies, diagnose diseases, detect pathogens or determine intracellular processes. In addition to bioimaging, these techniques are used for semi-quantitative and quantitative analyses. Some of them also enable highly valuable speciation of analytes. However, the quality of information about elemental tissue composition depends on a number of different factors. Although the crucial parameters of quantitative analysis are the same for each technique, their impact varies depending on the bioimaging method. Due to the fact that imaging results are often crucial in clinical decision-making, it is important to clearly indicate and describe the parameters affecting the quality of results in each technique. Therefore, the aim of this review is to describe the influence of these crucial parameters on bioimaging results based on the methodology and results of studies published in the last ten years. (2) Methods: In order to collect relevant publications, the Scopus database was searched using the keywords “element AND imaging AND human tissue”. Next, studies were selected in which methodological aspects allowed relevant conclusions to be made regarding the quality of the results obtained. (3) Results: One of the most important parameters for all techniques is measurement selectivity resulting from the complexity of human tissue. Quantitative analyses using bioimaging techniques are difficult due to the lack of suitable calibration materials. For the same reason, it is challenging to assess the accuracy of the results obtained. Particular attention should be paid to the results obtained for trace elements. (4) Conclusions: The discussed bioimaging techniques are a powerful tool in the elemental analysis of human tissues. Nevertheless, in order to obtain reliable results, a number of factors influencing the measurements must be taken into account. Full article

Explainable Artificial Intelligence (XAI) is increasingly important in computer vision, aiming to connect complex model outputs with human understanding. This review provides a focused comparative analysis of representative XAI methods in four main categories, attribution-based, activation-based, perturbation-based, and transformer-based approaches, selected from a broader literature landscape. Attribution-based methods like Grad-CAM highlight key input regions using gradients and feature activation. Activation-based methods analyze the responses of internal neurons or feature maps to identify which parts of the input activate specific layers or units, helping to reveal hierarchical feature representations. Perturbation-based techniques, such as RISE, assess feature importance through input modifications without accessing internal model details. Transformer-based methods, which use self-attention, offer global interpretability by tracing information flow across layers. We evaluate these methods using metrics such as faithfulness, localization accuracy, efficiency, and overlap with medical annotations. We also propose a hierarchical taxonomy to classify these methods, reflecting the diversity of XAI techniques. Results show that RISE has the highest faithfulness but is computationally expensive, limiting its use in real-time scenarios. Transformer-based methods perform well in medical imaging, with high IoU scores, though interpreting attention maps requires care. These findings emphasize the need for context-aware evaluation and hybrid XAI methods balancing interpretability and efficiency. The review ends by discussing ethical and practical challenges, stressing the need for standard benchmarks and domain-specific tuning. Full article

For the first time, the deposition area of heavy metals and other trace elements (Al, Ba, Cd, Co, Cr, Cu, Fe, Mn, Ni, P, Pb, S, Sr, Sb, V, Zn, and Hg) on the territory surrounding a landfill of domestic (municipal) waste at the 9th km of the Vilyuisky tract of Yakutsk within a radius of 51 km was assessed using the method of moss biomonitors and ICP-OES as an analytical technique. Mosses were analyzed for radionuclide content (⁴⁰K, ¹³⁷Cs, ²¹² Pb, ²¹⁴Pb, ²¹²Bi, ²¹⁴Bi, ²⁰⁸Tl, ⁷Be, and ²²⁸Ac) in a number of selected samples by semiconductor gamma spectrometry. The results of the examination of moss samples by ICP-OES indicate the presence of large amounts of toxic Ba and metal debris (Al, Co, Cr, Fe, S, and Pb) at the landfill. In addition, it is shown that the investigated samples contain elements such as Cd, Co, Cr, Cu, Cu, Mn, Ni, Pb, Sr, V, Zn, and Hg. The method of gamma spectrometry revealed that the studied samples contain such radioactive elements as ¹³⁷Cs, daughter products of ²³⁸U and ²³²Th. Detection of the same heavy metals and radionuclides in the atmospheric air of the city and in the vegetation near the landfill may indicate that one of the sources of environmental pollution may be products of incineration of the landfill contents at the 9th km of the Vilyuisky tract. Full article

The detection of particulate matter, particularly pathogenic bacteria, is essential in environmental monitoring, food safety, and clinical diagnostics. Among the various sensing techniques used, cantilever-based sensors offer a promising platform for label-free, real-time detection due to their high sensitivity. Here, we present a coupled cantilever sensor incorporating interdigitated comb-shaped structures to enhance dielectrophoretic (DEP) capture of Escherichia coli in liquid samples. During operation, one cantilever is externally actuated and the other oscillates passively through fluid-mediated coupling. The sensor was experimentally evaluated across a broad concentration range from 10 to 10⁵ cells/mL and the resonant frequency shifts were recorded for both beams. The results showed a strong linear frequency shift across all tested concentrations, without saturation. This demonstrates the sensor’s ability to detect both trace and high bacterial loads without needing recalibration. High frequency shifts of 4863 Hz were recorded for 10⁵ cells/mL and 225 Hz for the lowest concentration of 10 cells/mL, giving a limit of detection of 10 cells/mL. The sensor also showed a higher signal to noise ratio of 265.7 compared to previously reported designs. These findings showed that the enhanced sensor design enables sensitive, linear, and reliable bioparticle detection across a wide range, making it suitable for diverse applications. Full article

Sparkling wine is a complex alcoholic beverage with high economic value, produced through a secondary fermentation of a still wine, followed by a prolonged aging period that may last from nine months to several years. With the growing global demand for high-quality sparkling wines, understanding the biochemical mechanisms related to aroma development has become increasingly relevant. This review provides a comprehensive overview of the secondary fermentation process, with particular emphasis on yeast selection, types of closure, and the impact of aging on the volatile composition. Special attention is also given to the analytical strategies employed for the identification and quantification of target compounds in sparkling wine matrices. Due to the presence of volatile compounds at trace levels, effective extraction and pre-concentration techniques are essential. Extraction methods such as solid-phase microextraction (SPME), stir-bar sorptive extraction (SBSE), and thin-film SPME (TF-SPME) are discussed, as well as chromatographic techniques, such as gas chromatography (GC) and liquid chromatography (LC). Full article

The use of trees to revegetate urban areas contaminated by mining activity is a low-cost, low-maintenance technique, of which the success will depend on the plant species, planting methods, and geochemical processes at the soil-plant interface. This study analyzed the evolution of mineral composition in the rooting soil, tree, and herbaceous vegetation on soils contaminated by As, Cd, Cu, Co, Pb, and Zn. An in-situ experiment was carried out in Lubumbashi (South-eastern DR Congo) with six tree species (Acacia auriculiformis, Albizia lebbeck, Delonix regia, Leucaena leucocephala, Mangifera indica, and Syzygium guineense), in 0.187 m³ pits amended with municipal compost and limestone. After planting in the amended and unamended (control) pits, soil samples were taken for chemical analysis. Eighteen months after planting, a floristic inventory was carried out to assess the spontaneous colonization of herbaceous species. The results show an increase in metal concentrations in the rooting soil between 2019 and 2023 (Cu: 725 ± 136 to 6141 ± 1853 mg kg⁻¹; As: 16.2 ± 1.4 to 95 ± 28.5 mg kg⁻¹; Cd: 2.7 ± 1.3 to 8.7 ± 2.0 mg kg⁻¹; Co: 151 ± 36.3 to 182 ± 113 mg kg⁻¹; Zn: 558 ± 418 to 1098 ± 1037 mg kg⁻¹), with a stable pH and a decrease in nutrients (P, K, Ca, and Fe). The trees planted in the amended pits showed better height and diameter growth and greater survival than the controls, reaching average heights of 8 m and a DBH of up to 22 cm four years after planting. A total of 13 spontaneous herbaceous species were recorded, with an increased abundance during the second inventory. These results confirm the effectiveness of pit amendment for the rapid revegetation of urban soils polluted by trace metals. Full article

Molecular electronics studies have advanced from early, simple single-molecule experiments at cryogenic temperatures to complex and multifunctional molecules under ambient conditions. However, room-temperature environments increase the risk of contamination, making it essential to identify and quantify clean and contaminated rupture traces (i.e., conductance versus relative electrode displacement) within large datasets. Given the high throughput of measurements, manual analysis becomes unfeasible. Clustering algorithms offer an effective solution by enabling the automatic classification and quantification of contamination levels. Despite the rapid development of machine learning, its application in molecular electronics remains limited. In this work, we present a methodology based on the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm to extract representative traces from both clean and contaminated regimes, providing a scalable and objective tool to evaluate environmental contamination in molecular junction experiments. Full article

This study systematically reviews the development and application of remote sensing technology in monitoring and evaluating urban heat island (UHI) effects. The urban heat island effect, characterized by significantly higher temperatures in urban areas compared to surrounding rural regions, has become a widespread environmental issue globally, with impacts spanning public health, energy consumption, ecosystems, and social equity. The paper first analyzes the formation mechanisms and impacts of urban heat islands, then traces the evolution of remote sensing technology from early traditional platforms such as Landsat and NOAA-AVHRR to modern next-generation systems, including the Sentinel series and ECOSTRESS, emphasizing improvements in spatial and temporal resolution and their application value. At the methodological level, the study systematically evaluates core algorithms for land surface temperature extraction and heat island intensity calculation, compares innovative developments in multi-source remote sensing data integration and fusion techniques, and establishes a framework for accuracy assessment and validation. Through analyzing the heat island differences between metropolitan areas and small–medium cities, the relationship between urban morphology and thermal environment, and regional specificity and global universal patterns, this study revealed that the proportion of impervious surfaces is the primary driving factor of heat island intensity while simultaneously finding that vegetation cover exhibits significant cooling effects under suitable conditions, with the intensity varying significantly depending on vegetation types, management levels, and climatic conditions. In terms of applications, the paper elaborates on the practical value of remote sensing technology in identifying thermally vulnerable areas, green space planning, urban material optimization, and decision support for UHI mitigation. Finally, in light of current technological limitations, the study anticipates the application prospects of artificial intelligence and emerging analytical methods, as well as trends in urban heat island monitoring against the backdrop of climate change. The research findings not only enrich the theoretical framework of urban climatology but also provide a scientific basis for urban planners, contributing to the development of more effective UHI mitigation strategies and enhanced urban climate resilience. Full article