Search Results (1,688)

Introduction: Artificial intelligence (AI) has developed into an increasingly important tool in dermatology. While new technologies integrated within laser devices are emerging, there is a lack of data on the applicability of publicly available AI models. Methods: The prospective study used an online questionnaire where participants evaluated diagnosis and treatment for 25 dermatological cases shown as pictures. The same questions were given to AI models: ChatGPT-4o, Claude Sonnet 4, Gemini 2.5 Flash, and Grok-3. Results: Dermatologists outperformed AI in diagnostic accuracy (suspected primary diagnosis-SD 75.6%) in pooled dermatologists vs. pooled AI (SD 57.0%), with laser specialists achieving the highest accuracy (SD 82.0%) and residents the lowest (SD 66.0%). There was a high heterogeneity across AI models. Gemini approached dermatologist performance (SD 72.0%), while Claude showed a low accuracy (SD 40.0%). While AI models reached near 100% accuracy in some classic/common diagnoses (e.g., acne, rosacea, spider angioma, infantile hemangioma), their accuracy dropped to near 0% on rare or context-dependent cases (e.g., blue rubber bleb nevus syndrome, angiosarcoma, hirsutism, cutaneous siderosis). Inter-rater agreement was high among laser experts in terms of diagnostic accuracy and treatment choice. Agreement between residents and AI models was highest for diagnostic accuracy and treatment choice, while it was lowest between experts and AI models. Conclusions: Before AI-based tools can be integrated into daily practice, particularly regarding diagnosis and appropriate laser treatment recommendations, specific supervised medical training of the AI model is necessary, as open-source platforms currently lack the ability to contextualize presented data. Full article

The increasing impact of global warming is predominantly driven by the extensive use of fossil fuels, which release significant amounts of greenhouse gases into the atmosphere. This has led to a critical need for alternative, sustainable energy sources that can mitigate environmental impacts. Photovoltaic technology has emerged as a promising solution by harnessing renewable energy from the sun, providing a clean and inexhaustible power source. Perovskite solar cells (PSCs) are a class of hybrid organic–inorganic solar cells that have recently attracted significant scientific attention due to their low cost, relatively high efficiency, low–temperature processing routes, and longer carrier lifetimes. These characteristics make them a viable alternative to traditional fossil fuels, reducing the carbon footprint and contributing to the fight against global warming. In this study, the SCAPS–1D numerical simulator was used in the computational analysis of a PSC device with the configuration FTO/ETL/BaZr(S_0.6Se_0.4)₃/HTL/Ir. Different hole transport layer (HTL) and electron transport layer (ETL) material were proposed and tested. The HTL materials included copper (I) oxide (Cu₂O), 2,2′,7,7′–Tetrakis(N,N–di–p–methoxyphenylamine)9,9′–spirobifluorene (spiro–OMETAD), and poly(3–hexylthiophene) (P₃HT), while the ETLs included cadmium suphide (CdS), zinc oxide (ZnO), and [6,6]–phenyl–C₆₁–butyric acid methyl ester (PCBM). Finally, BaZr(S_0.6Se_0.4)₃ was proposed as an absorber, and a fluorine–doped tin oxide glass substrate (FTO) was proposed as an anode. The metal back contact used was iridium. Photovoltaic parameters such as short circuit density (I_sc), open circuit voltage (V_oc), fill factor (FF), and power conversion efficiency (PCE) were used to evaluate the performance of the device. The initial simulated primary device with the configuration FTO/CdS/BaZr(S_0.6Se_0.4)₃/spiro–OMETAD/Ir gave a PCE of 5.75%. Upon testing different HTL materials, the best HTL was found to be Cu₂O, and the PCE improved to 9.91%. Thereafter, different ETLs were also inserted and tested, and the best ETL was established to be ZnO, with a PCE of 10.10%. Ultimately an optimized device with a configuration of FTO/ZnO/BaZr(S_0.6Se_0.4)₃/Cu₂O/Ir was achieved. The other photovoltaic parameters for the optimized device were as follows: FF = 31.93%, J_sc = 14.51 mA cm⁻², and V_oc = 2.18 V. The results of this study will promote the use of environmentally benign BaZr(S_0.6Se_0.4)₃–based absorber materials in PSCs for improved performance and commercialization. Full article

Remote sensing technology has become an indispensable core means for Earth observation. As two of the most commonly used remote sensing modalities, the fusion of optical and synthetic aperture radar (SAR) (OPT-SAR fusion) can effectively overcome the limitations of a single data source, achieve information complementarity and synergistic enhancement, thereby significantly improving the interpretation capability of multi-source remote sensing data. This paper first discusses the necessity of OPT-SAR fusion, systematically reviews the historical development of fusion technologies, and summarizes open-source resources for various tasks, aiming to provide a reference for related research. Finally, building upon recent advances in OPT-SAR fusion research and cutting-edge developments in deep learning, this paper proposes that future fusion technologies should develop in the following directions: interpretable fusion models driven by both data and knowledge, general fusion perception driven by multimodal large models, and lightweight architectures with efficient deployment strategies. Full article

Project control in cost estimation, time scheduling, and resource accounting remains challenging, particularly when using the open-source Industry Foundation Classes (IFCs) format. This study aims to define the state of the art in integrating these three domains. A systematic literature review was conducted, using a bibliometric analysis to map and interpret scientific knowledge and research trajectories, and an inductive analysis for a detailed examination of relevant studies. The analysis highlights a lack of clarity in applying the IFC standard across project control domains, as current practices often rely on non-standardized procedures, including incorrect use of classes or properties, creation of unneeded user-defined PropertySets and properties, or reliance on proprietary software. Integration of cost, time, and resource management remains limited, and proposed technological solutions generally require coding skills that typical professionals do not possess. Additional challenges include fragmented data across multiple databases, manual assignment of time, cost, and resource information, and limited collaboration, all of which are time-consuming and error-prone. There is a critical need for clearer guidelines on IFC usage to enable standardized procedures and facilitate the development of IFC-based tools. Automating these labor-intensive tasks could improve efficiency, reduce errors, and support broader adoption of integrated project control practices. Full article

Accurate and efficient forest inventory methods are crucial for monitoring forest ecosystems, assessing carbon stocks, and supporting sustainable forest management. Traditional field-based techniques, which rely on manual measurements such as diameter at breast height (DBH) and tree height (TH), remain labour-intensive and time-consuming. In this study, we introduce and validate a fully open-source, low-cost terrestrial laser scanning system (LCA-TLS) built from commercially available components and based on the Livox Avia sensor. With a total cost of €2050, the system responds to recent technological developments that have significantly reduced hardware expenses while retaining high data quality. This trend has created new opportunities for broadening access to high-resolution 3D data in ecological research. The performance of the LCA-TLS was assessed under controlled and field conditions and benchmarked against three reference devices: the RIEGL VZ-1000 terrestrial laser scanner, the Stonex X120GO handheld mobile laser scanner, and the iPhone 15 Pro Max structured-light device. The LCA-TLS achieved high accuracy for estimating DBH (RMSE: 1.50 cm) and TH (RMSE: 0.99 m), outperforming the iPhone and yielding results statistically comparable to the Stonex X120GO (DBH RMSE: 1.32 cm; p > 0.05), despite the latter being roughly ten times more expensive. While the RIEGL system produced the most accurate measurements, its cost exceeded that of the LCA-TLS by a factor of about 30. The hardware design, control software, and processing workflow of the LCA-TLS are fully open-source, allowing users worldwide to build, modify, and apply the system with minimal resources. The proposed solution thus represents a practical, cost-effective, and accessible alternative for 3D forest inventory and LiDAR-based ecosystem monitoring. Full article

In Mexico, the Deaf community faces persistent communication barriers that restrict their integration and access to essential services, particularly in healthcare. Even though approximately two million individuals use Mexican Sign Language (MSL) as their primary form of communication, technological tools for supporting effective interaction remain limited. While recent research in sign-language recognition has led to important advances for several languages, work focused on MSL, particularly for healthcare scenarios, remains scarce. To address this gap, this study introduces a health-oriented dataset of 150 signs, with 800 synthetic video sequences per word, totaling more than 35 GB of data. This dataset was used to train recurrent neural networks with regularization and data augmentation. The best configuration achieved a maximum precision of 98.36% in isolated sign classification, minimizing false positives, which is an essential requirement in clinical applications. Beyond isolated recognition, the main contribution of this study is its exploratory evaluation of sequential narrative inference in MSL. Using short scripted narratives, the system achieved a global sequential recall of 45.45% under a realistic evaluation protocol that enforces temporal alignment. These results highlight both the potential of recurrent architectures in generalizing from isolated gestures to structured sequences and the substantial challenges posed by continuous signing, co-articulation, and signer-specific variation. While not intended for clinical deployment, the methodology, dataset, and open-source implementation presented here establish a reproducible baseline for future research. This work provides initial evidence, tools, and insights to support the long-term development of accessible technologies for the Deaf community in Mexico. Full article

This study focuses on a rapidly urbanizing coastal plain where river networks serve as critical pathways for pollutant transport to nearshore waters. Under frequent sluice control and sluggish hydrodynamics, pollutants accumulate in channels and are subsequently flushed during intense rainfall or sluice-opening events, increasing pollutant loads in downstream estuaries. Based on 2017–2024 water quality monitoring data, integrated multi-source environmental factor analysis and unmanned patrol boat technology, systematic water quality assessment and pollution source identification were conducted. Significant spatial heterogeneity was observed: phosphorus and nitrogen pollution dominated in the eastern region, whereas the permanganate index was more prominent in the western part of the network. Identification of abrupt water quality change sections revealed industrial wastewater as the primary contributor to phosphorus and nitrogen, whereas permanganate index pollution originated widely from aquaculture, agriculture, and industrial discharges. Atmospheric deposition likely provides a non-negligible contribution to phosphorus and nitrogen input, with fluxes strongly correlated to rainfall. Sediment release posed internal risks of carbon and phosphorus, with intensity positively linked to pollution levels. This study elucidates the water quality characteristics and multi-source pollution mechanisms in typical coastal river networks under rapid economic development. Therefore, it provides a scientific basis for precise regional water environment management and coastal water quality protection. Full article

The concept of sustainability in the supermarket sector has emerged as a strategic priority, as companies are required to reduce their environmental footprint and enhance their social and economic performance. The aim of this literature review is to identify, document, and analyze the key performance indicators (KPIs) applied in the sector, with emphasis on environmental, social, and economic dimensions, and to investigate the extent to which technical energy interventions are linked to business and consumer benefits. The methodology was inspired by the general logic of organized search and selection procedures, and for this reason, elements of the PRISMA framework were used, with a search conducted across multiple international scientific databases and selection criteria ensuring the validity and relevance of the sources. The analysis classified the indicators into the following three categories: environmental (e.g., CO₂ emissions, energy consumption), social (e.g., customer satisfaction, corporate image), and economic (e.g., ESG score, return on investment). The study revealed substantial progress made by supermarket chains globally in adopting energy-efficiency technologies, such as LED lighting and renewable energy with proven benefits in reducing consumption and consequently, improving environmental performance. However, a lack of holistic integration between technical interventions and social-economic indicators was identified, limiting the use of KPIs as a strategic tool for guiding specific sustainability strategies. This research concludes that there is a need to develop unified, sector-specific measurement frameworks that integrate environmental, social, and economic parameters, as well as empirical research that quantitatively connects energy strategies with business and consumer performance through comparable indicators in the context of supermarket operations, thereby opening ground for further exploration of the field. Full article

This work introduces a low-cost, wearable assistive mouse designed to support digital interaction for individuals with motor impairments. The system combines inertial sensing for head-movement tracking and a pressure-based interface for simulating mouse clicks via “sip-and-puff” actions. The device enables full mouse control (pointer movement, clicks, and double-clicks) without relying on hand mobility. Preliminary evaluations, conducted with input from occupational therapy professionals, demonstrated promising usability and functionality comparable to commercial devices. The proposed solution offers a cost-effective, open-source alternative to existing adaptive technologies, with future development aimed at broader testing and integration in rehabilitation settings. Future work will include usability testing with individuals presenting real motor impairments to validate clinical applicability. Full article

With the rapid development of remote sensing technology and deep learning, the port ship detection based on a single-stage algorithm has achieved remarkable results in optical imagery. However, most of the existing methods are designed and verified in specific scenes, such as fixed viewing angle, uniform background, or open sea, which makes it difficult to deal with the problem of ship detection in complex environments, such as cloud occlusion, wave fluctuation, complex buildings in the harbor, and multi-ship aggregation. To this end, ES-YOLO framework is proposed to solve the limitations of ship detection. A novel edge perception channel, Spatial Attention Mechanism (EACSA), is proposed to enhance the extraction of edge information and improve the ability to capture feature details. A lightweight spatial–channel decoupled down-sampling module (LSCD) is designed to replace the down-sampling structure of the original network and reduce the complexity of the down-sampling stage. A new hierarchical scale structure is designed to balance the detection effect of different scale differences. In this paper, a remote sensing ship dataset, TJShip, is constructed based on Gaofen-2 images, which covers multi-scale targets from small fishing boats to large cargo ships. The TJShip dataset was adopted as the data source, and the ES-YOLO model was employed to conduct ablation and comparison experiments. The results show that the introduction of EACSA attention mechanism, LSCD, and multi-scale structure improves the mAP of ship detection by 0.83%, 0.54%, and 1.06%, respectively, compared with the baseline model, also performing well in precision, recall and F1. Compared with Faster R-CNN, RetinaNet, YOLOv5, YOLOv7, and YOLOv8 methods, the results show that the ES-YOLO model improves the mAP by 46.87%, 8.14%, 1.85%, 1.75%, and 0.86%, respectively, under the same experimental conditions, which provides research ideas for ship detection. Full article

Smart cities seek to improve urban living by embedding advanced technologies into infrastructures, services, and governance. Edge Artificial Intelligence (Edge AI) has emerged as a critical enabler by moving computation and learning closer to data sources, enabling real-time decision-making, improving privacy, and reducing reliance on centralized cloud infrastructure. This survey provides a comprehensive review of the foundations, challenges, and opportunities of edge AI in smart cities. In particular, we begin with an overview of layer-wise designs for edge AI-enabled smart cities, followed by an introduction to the core components of edge AI systems, including applications, sensing data, models, and infrastructure. Then, we summarize domain-specific applications spanning manufacturing, healthcare, transportation, buildings, and environments, highlighting both the softcore (e.g., AI algorithm design) and the hardcore (e.g., edge device selection) in heterogeneous applications. Next, we analyze the sources of sensing data generation, model design strategies, and hardware infrastructure that underpin edge AI deployment. Building on these, we finally identify several open challenges and provide future research directions in this domain. Our survey outlines a future research roadmap to advance edge AI technologies, thereby supporting the development of adaptive, harmonic, and sustainable smart cities. Full article

Early detection of breast cancer commonly relies on imaging technologies such as ultrasound, mammography and MRI. Among these, breast ultrasound is widely used by radiologists to identify and assess lesions. In this study, we developed image segmentation techniques and multiclass classification artificial intelligence (AI) tools based on pretrained models to segment lesions and detect breast cancer. The proposed workflow includes both the development of segmentation models and development of a series of classification models to classify ultrasound images as normal, benign or malignant. The pretrained models were trained and evaluated on the Breast Ultrasound Images (BUSI) dataset, a publicly available collection of grayscale breast ultrasound images with corresponding expert-annotated masks. For segmentation, images and ground-truth masks were used to pretrained encoder (ResNet18, EfficientNet-B0 and MobileNetV2)–decoder (U-Net, U-Net++ and DeepLabV3) models, including the DeepLabV3 architecture integrated with a Frequency-Domain Feature Enhancement Module (FEM). The proposed FEM improves spatial and spectral feature representations using Discrete Fourier Transform (DFT), GroupNorm, dropout regularization and adaptive fusion. For classification, each image was assigned a label (normal, benign or malignant). Optuna, an open-source software framework, was used for hyperparameter optimization and for the testing of various pretrained models to determine the best encoder–decoder segmentation architecture. Five different pretrained models (ResNet18, DenseNet121, InceptionV3, MobielNetV3 and GoogleNet) were optimized for multiclass classification. DeepLabV3 outperformed other segmentation architectures, with consistent performance across training, validation and test images, with Dice Similarity Coefficient (DSC, a metric describing the overlap between predicted and true lesion regions) values of 0.87, 0.80 and 0.83 on training, validation and test sets, respectively. ResNet18:DeepLabV3 achieved an Intersection over Union (IoU) score of 0.78 during training, while ResNet18:U-Net++ achieved the best Dice coefficient (0.83) and IoU (0.71) and area under the curve (AUC, 0.91) scores on the test (unseen) dataset when compared to other models. However, the proposed Resnet18: FrequencyAwareDeepLabV3 (FADeepLabV3) achieved a DSC of 0.85 and an IoU of 0.72 on the test dataset, demonstrating improvements over standard DeepLabV3. Notably, the frequency-domain enhancement substantially improved the AUC from 0.90 to 0.98, indicating enhanced prediction confidence and clinical reliability. For classification, ResNet18 produced an F1 score—a measure combining precision and recall—of 0.95 and an accuracy of 0.90 on the training dataset, while InceptionV3 performed best on the test dataset, with an F1 score of 0.75 and accuracy of 0.83. We demonstrate a comprehensive approach to automate the segmentation and multiclass classification of breast cancer ultrasound images into benign, malignant or normal transfer learning models on an imbalanced ultrasound image dataset. Full article

Livestock farming represents one of the primary sources of ammonia (NH₃) and greenhouse gas (GHG) emissions, including methane (CH₄), nitrous oxide (N₂O), and carbon dioxide (CO₂), having a significant environmental impact. Reducing emissions and recovering gas systems from these livestock buildings necessitate measuring gas concentrations to mitigate environmental impacts using an accurate, high-cost portable device. This study aims to evaluate the concentration of NH₃ and GHGs in a semi-open dairy farm located in southern Sicily, a region with a hot climate. The measurement campaign was carried out during the spring of 2025. The concentrations of NH₃, CH₄, CO_2, and N₂O were measured in different barn areas (i.e., manger, feeding alley, and service alley) using a portable gas detector (GASMET GT5000) based on Fourier Transform Infrared (FTIR) technology. Statistical analysis revealed that NH₃ concentrations were highest in the feeding alley, while CH₄ concentrations peaked at the manger. N₂O levels stayed low because there was no straw. Future research should investigate gas concentrations across different seasons (e.g., winter, summer) to analyze gas patterns under different climatic conditions. Additionally, the use of an accurate portable device enables further investigations into other barn typologies within the Mediterranean area to assess how farm construction and management practices influence gas production. Full article

The main objective of this article is to conduct a literature review on the use of open innovation (OI) for green transition to identify tools and methods that can make green transition more effective, efficient, and socially acceptable. This review is accompanied by an attempt to answer the following research questions: R1. How can open innovation be used in the economy and by individual entities to achieve the goals of the green transition? R2. How can individual stakeholders be activated and motivated to participate in the process of creating open innovation for the green transition? and R3. What are the real effects of using open innovation on a macroeconomic, social, and individual scale? The results allow concluding that OI is used by enterprises, cities, regions, and entire economies. Among the methods of activating and motivating individual stakeholders to engage in the process of creating OI for green transition, the following can be selected: (1) internal resources and competencies (knowledge management, internal programs, open leadership, trust, complementarity of resources); (2) partnership characteristics (modern business models, involvement of partnership intermediaries, strengthening relationships with suppliers and customers, involvement of prosumers, cooperation with universities and research institutions); (3) external legal and regulatory conditions (protection of intellectual property rights, pro-innovation and pro-environmental education systems, creation of a legal framework for cooperation between science and business); and (4) external technical and organizational solutions (online platforms, social media, Living Labs, external sources of knowledge). The most frequently mentioned individual effects of open innovation in the energy sector include: improved efficiency, effectiveness and competitiveness in environmental management and the implementation of sustainable development, as well as the use of modern technologies. At the economic level, OI supports investment and economic growth. It can also have a positive impact on reducing energy poverty and developing renewable energy sources, including in emerging economies. This form of innovation also promotes social integration and the creation of social values. The findings of this review can be utilized by scholars to identify current and future research directions. They may also prove valuable for practitioners as both an incentive to engage in open innovation and guidance for its design and implementation. Furthermore, the results can contribute to disseminating knowledge about open innovation and its role in the green transformation. Full article

Artifacts remain a major challenge in electroencephalogram (EEG) recordings, often degrading the accuracy of clinical diagnosis, brain computer interface (BCI) systems, and cognitive research. Although recent deep learning approaches have advanced EEG denoising, most still struggle to model long-range dependencies, maintain computational efficiency, and generalize to unseen artifact types. To address these challenges, this study proposes MDSC-VA, an efficient denoising framework that integrates multi-scale (M) depth-wise separable convolution (DSConv), variational autoencoder-based (VAE) latent encoding, and a multi-head self-attention mechanism. This unified architecture effectively balances denoising accuracy and model complexity while enhancing generalization to unseen artifact types. Comprehensive evaluations on three open-source EEG datasets, including EEGdenoiseNet, a Motion Artifact Contaminated Multichannel EEG dataset, and the PhysioNet EEG Motor Movement/Imagery dataset, demonstrate that MDSC-VA consistently outperforms state-of-the-art methods, achieving a higher signal-to-noise ratio (SNR), lower relative root mean square error (RRMSE), and stronger correlation coefficient (CC) values. Moreover, the model preserved over 99% of the dominant neural frequency band power, validating its ability to retain physiologically relevant rhythms. These results highlight the potential of MDSC-VA for reliable clinical EEG interpretation, real-time BCI systems, and advancement towards sustainable healthcare technologies in line with SDG-3 (Good Health and Well-Being). Full article