Ensuring a consistently reliable power supply is paramount in power systems. Researchers are engaged in the pursuit of categorizing transmission line failures to design countermeasures for mitigating the associated financial losses. Our study employs a machine learning-based methodology, specifically the Conformer Convolution-Augmented Transformer model, to classify transmission line fault types. This model processes time series input data directly, eliminating the need for expert feature extraction. The training and validation datasets are generated through simulations conducted on a two-terminal transmission line, while testing is conducted on historical data consisting of 108 events that occurred in the Taiwan power system. Due to the limited availability of historical data, they are utilized solely for inference purposes. Our simulations are meticulously designed to encompass potential faults based on an analysis of historical data. A significant aspect of our investigation focuses on the impact of the sampling rate on input data, establishing that a rate of four samples per cycle is sufficient. This suggests that, for our specific classification tasks, relying on lower frequency data might be adequate, thereby challenging the conventional emphasis on high-frequency analysis. Eventually, our methodology achieves a validation accuracy of 100%, although the testing accuracy is lower at 88.88%. The discrepancy in testing accuracy can be attributed to the limited information and the small number of historical events, which pose challenges in bridging the gap between simulated data and real-world measurements. Furthermore, we benchmarked our method against the ELM model proposed in 2023, demonstrating significant improvements in testing accuracy. Full article

The reconstruction of bone deficiencies remains a challenge due to the limitations of autologous bone grafting. The objective of this study is to evaluate the bone regeneration efficacy of additive manufacturing of tricalcium phosphate (TCP) implants using lithography-based ceramic manufacturing (LCM). LCM uses LithaBone TCP 300 slurry for 3D printing, producing cylindrical scaffolds. Four models of internal scaffold geometry were developed and compared. The in vitro studies included cell culture, differentiation, seeding, morphological studies and detection of early osteogenesis. The in vivo studies involved 42 Wistar rats divided into four groups (control, membrane, scaffold (TCP) and membrane with TCP). In each animal, unilateral right mandibular defects with a total thickness of 5 mm were surgically performed. The animals were sacrificed 3 and 6 months after surgery. Bone neoformation was evaluated by conventional histology, radiology, and micro-CT. Model A (spheres with intersecting and aligned arrays) showed higher penetration and interconnection. Histological and radiological analysis by micro-CT revealed increased bone formation in the grafted groups, especially when combined with a membrane. Our innovative 3D printing technology, combined with precise scaffold design and efficient cleaning, shows potential for bone regeneration. However, further refinement of the technique and long-term clinical studies are crucial to establish the safety and efficacy of these advanced 3D printed scaffolds in human patients. Full article

In recent decades, fuzzy logic and fuzzy multi-criteria decision-making systems have been applied in several fields. This paper aims to determine the optimal wind farm siting solution in a fuzzy environment. Therefore, the main research question of the present paper is whether and to what extent the uncertainty in the researcher’s judgments affects the ranking of wind farm siting solutions. The fuzzy analytical hierarchy method is applied to an existing case study of wind farm siting on the island of Andros, examining the stability of the final priorities of the alternatives under a regime of gradual increases in ambiguity, as well as whether the introduced ambiguity in the model corresponds to any uncertainty the researcher has during the process of scoring the criteria and alternatives. Five assessment criteria (wind potential, ground slope, distance from road network, distance from high-voltage network, and social acceptance of local population) and eight eligible suitable alternatives (A1–A8) for wind farm siting are considered in the computations. The methodology includes the fuzzification of initial decision-maker judgments, the calculation of fuzzy intermediate priorities (weights), the defuzzification of fuzzy intermediate priorities (weights), and the synthesis of intermediate priorities into final priorities of alternatives, according to the procedures of the crisp AHP (CAHP). Under the assumptions of the initial case study, the results show that the final priorities are quite robust when faced with increased ambiguity. In almost all the examined cases, the alternative initially chosen as the best, A1, is dominant, followed by A3. In addition, in all cases, social acceptance favors alternative A1, and wind velocity favors alternative A8. Therefore, fuzzy multi-criteria methods can be applied to determine an optimal wind farm siting solution when criteria with qualitative characteristics are used and the manifestation of preferences involves strong elements of subjectivity. Full article

North Africa, the Middle East, and Europe (NAMEE domain) host a variety of suspended particles characterized by different optical and microphysical properties. In the current study, we investigate the importance of the lidar ratio (LR) on Cloud-Aerosol Lidar with Orthogonal Polarization–Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIOP-CALIPSO) aerosol retrievals towards assessing aerosols’ impact on the Earth-atmosphere radiation budget. A holistic approach has been adopted involving collocated Aerosol Robotic Network (AERONET) observations, Radiative Transfer Model (RTM) simulations, as well as reference radiation measurements acquired using spaceborne (Clouds and the Earth’s Radiant Energy System-CERES) and ground-based (Baseline Surface Radiation Network-BSRN) instruments. We are assessing the clear-sky shortwave (SW) direct radiative effects (DREs) on 550 atmospheric scenes, identified within the 2007–2020 period, in which the primary tropospheric aerosol species (dust, marine, polluted continental/smoke, elevated smoke, and clean continental) are probed using CALIPSO. RTM runs have been performed relying on CALIOP retrievals in which the default and the DeLiAn (Depolarization ratio, Lidar ratio, and Ångström exponent)-based aerosol-speciated LRs are considered. The simulated fields from both configurations are compared against those produced when AERONET AODs are applied. Overall, the DeLiAn LRs leads to better results mainly when mineral particles are either solely recorded or coexist with other aerosol species (e.g., sea-salt). In quantitative terms, the errors in DREs are reduced by ~26–27% at the surface (from 5.3 to 3.9 W/m²) and within the atmosphere (from −3.3 to −2.4 W/m²). The improvements become more significant (reaching up to ~35%) for moderate-to-high aerosol loads (AOD ≥ 0.2). Full article

In decentralized systems, the quest for heightened security and integrity within blockchain networks becomes an issue. This survey investigates anomaly detection techniques in blockchain ecosystems through the lens of unsupervised learning, delving into the intricacies and going through the complex tapestry of abnormal behaviors by examining avant-garde algorithms to discern deviations from normal patterns. By seamlessly blending technological acumen with a discerning gaze, this survey offers a perspective on the symbiotic relationship between unsupervised learning and anomaly detection by reviewing this problem with a categorization of algorithms that are applied to a variety of problems in this field. We propose that the use of unsupervised algorithms in blockchain anomaly detection should be viewed not only as an implementation procedure but also as an integration procedure, where the merits of these algorithms can effectively be combined in ways determined by the problem at hand. In that sense, the main contribution of this paper is a thorough study of the interplay between various unsupervised learning algorithms and how this can be used in facing malicious activities and behaviors within public and private blockchain networks. The result is the definition of three categories, the characteristics of which are recognized in terms of the way the respective integration takes place. When implementing unsupervised learning, the structure of the data plays a pivotal role. Therefore, this paper also provides an in-depth presentation of the data structures commonly used in unsupervised learning-based blockchain anomaly detection. The above analysis is encircled by a presentation of the typical anomalies that have occurred so far along with a description of the general machine learning frameworks developed to deal with them. Finally, the paper spotlights challenges and directions that can serve as a comprehensive compendium for future research efforts. Full article

Maintaining balance is critical to minimizing astronauts’ risk of falling and reducing injury or suit damage. Previous studies involving spacesuits have not examined the effects of the superior shift of the center of gravity (CoG) on astronauts’ ability to balance. Here, the purpose of our study was to investigate the effects of CoG shift due to a simulated Extravehicular Mobility Unit (xEMU) on balance. Seventeen participants’ standing balance was examined for three test configurations: unsuited, weighted with an Extravehicular Mobility Unit (xEMU) vest, and xEMU hard upper body torso (HUT). Using a Tekscan forceplate walkway, the CoG locations were determined. Balance assessments were performed to determine the limits of stability and standing balance performance during wide or tandem stances with eyes open/closed. The center of pressure (CoP) time series was examined in terms of displacement, velocity, and frequency measures. During the eyes-open wide stance, the xEMU vest significantly increased the mediolateral balance parameters, while the HUT significantly increased the total displacement (TOTEX), mean velocity (MVELO), and mean frequency (MFREQ) of the CoP. In the eyes-closed wide stance, the HUT significantly increased these parameters. In the eyes-closed tandem stance, the xEMU vest significantly decreased the parameters. The xEMU vest significantly reduced the TOTEX, MVELO, and MFREQ (improved standing balance), while the HUT decreased standing balance ability, seen with significant increases in said parameters. By quantifying CoG’s effect on balance, our results form the basis for future balance and posture studies of xEMU spacesuits. Full article

This study develops a vitamin C controlled-release system, trackable via color changes as a function of vitamin C release. The system is composed of coaxial microfibers prepared via coaxial electrospinning, with a core of poly(ethylene oxide) (PEO) incorporating vitamin C, and a shell composed of polycaprolactone (PCL) containing polydiacetylene (PDA) as the color-changing material. The shell thickness is controlled by adjusting the amount of PCL ejected during electrospinning, allowing regulation of the release rate of vitamin C. When vitamin C added to PEO penetrates the PCL layer, the color of PDA changes from blue to red, indicating a color change. The results of this study can be applied to devices that require immediate detection of vitamin C release levels. Full article

To investigate the impact of structural damages on the comfort level of suspension footbridges under human-induced vibrations, this study addresses the limitations of traditional manual testing, which often entails significant manpower and material resources. The aim is to achieve rapid estimation and health monitoring of comfort levels during bridge operation. To accomplish this, the study combines finite-element simulation results to establish a data-driven library and introduces three distinct machine learning algorithms. Through comparative analysis, a machine learning-based method is proposed for quick evaluation of bridge comfort levels. Focusing on the Yangjiadong Suspension Bridge, the study evaluates and researches the comfort level of the structure under the influence of human-induced vibrations. The findings revealed a relatively low base frequency and high flexibility. Additionally, when considering the mass of individuals, peak acceleration decreased. The predictive performance of the Artificial Neural Network (ANN) model was found to be superior when accounting for multi-parameter damages, yielding root mean square error (RMSE), mean absolute percentage error (MAPE), and R-squared (R²) values of 0.03, 0.02, and 0.98, respectively. Moreover, the error ratio of the generalization performance analysis was below 5%. Furthermore, the study identified a damage coefficient of 0.13 for the bridge’s main cable, hanger, and steel longitudinal beam. Under a crowd density of 0.5 people per square meter, the predicted peak acceleration was 1.098 m/s², with a model error of less than 10% compared to the observed value of 1.004 m/s². These results underscore the model’s effectiveness in swiftly evaluating bridge comfort levels, thereby offering valuable insights for the health monitoring of bridge comfort levels. Full article

The scope of this work is the development of a method to estimate the temperature and shear rate-dependent viscosity of mixtures composed of two polymers. The viscosity curve of polymer mixtures is crucial for the modeling and optimization of extrusion-based recycling, which is the most efficient way to recycle polymeric materials. The modeling and simulation of screw extruders requires detailed knowledge of the properties of the processed material, such as the thermodynamic properties, the density, and the rheological behavior. These properties are widely known for pure materials; however, the incorporation of impurities, like other polymers in recycled materials, alters the properties. In this work, miscible, immiscible, and compatibilized immiscible polymer mixtures are considered. A new method based on shear stress is proposed and compared to the shear rate-based method. Several mixing rules are evaluated for their accuracy in predicting mixture viscosity. The developed methods allow the prediction of the viscosity of a compatibilized immiscible mixture with deviations below 5% and that of miscible polymer mixtures with deviations below 3.5%. Full article

In this presented study, we measured in situ the uplink duty cycles of a smartphone for 5G NR and 4G LTE for a total of six use cases covering voice, video, and data applications. The duty cycles were assessed at ten positions near a 4G and 5G base-station site in Belgium. For Twitch, VoLTE, and WhatsApp, the duty cycles ranged between 4% and 22% in time, both for 4G and 5G. For 5G NR, these duty cycles resulted in a higher UL-allotted time due to time division duplexing at the 3.7 GHz frequency band. Ping showed median duty cycles of 2% for 5G NR and 50% for 4G LTE. FTP upload and iPerf resulted in duty cycles close to 100%. Full article

Data augmentation is considered a promising technique to resolve the imbalance of large and small objects. Unfortunately, most existing methods augment all small objects indiscriminately, regardless of their learnability and proportion. This tends to result in wasteful enlargement for many weak, low-information objects but under-augmentation for rare and learnable objects. To this end, we propose a value-guided adaptive data augmentation for scale- and proportion-imbalanced small object detection (ValCopy-Paste). Specifically, we first develop a non-learning object value criteria to determine whether one object should be expanded. Both scale-based learnability and quantity-based necessity are involved in this criteria. Then, the value distribution of objects in the dataset can be further constructed on the basis of the relevant object values. This helps to ensure that those uncommon, learnable objects that deserve enhancement are more likely to be enhanced. Additionally, we propose to enhance the data by pasting the sampled objects into relatively smooth portions of fresh background images, rather than arbitrary areas of any background images. This helps to boost data diversity while reducing the interference from complicated backgrounds. Evidently, our method does not require sophisticated training and just depends on the size and distribution of the objects in the dataset. Extensive experiments on MS COCO 2017 and PASCAL VOC 2012 demonstrate that our method achieves better performance than state-of-the-art methods. Full article

Copy number variation (CNV) is an important structural variation used to elucidate complex economic traits. In this study, we sequenced 25 Wannan spotted pigs (WSPs) to detect their CNVs and identify their selection signatures compared with those of 10 Asian wild boars. A total of 14,161 CNVs were detected in the WSPs, accounting for 0.72% of the porcine genome. The fixation index (Fst) was used to identify the selection signatures, and 195 CNVs with the top 1% of the Fst value were selected. Eighty genes were identified in the selected CNV regions. Functional GO and KEGG analyses revealed that the genes within these selected CNVs are associated with key traits such as reproduction (GAL3ST1 and SETD2), fatty acid composition (PRKG1, ACACA, ACSL3, UGT8), immune system (LYZ), ear size (WIF1), and feed efficiency (VIPR2). The findings of this study contribute novel insights into the genetic CNVs underlying WSP characteristics and provide essential information for the protection and utilization of WSP populations. Full article

The use of modern spectroscopic methods of analysis, which provide extensive information on the chemical nature of substances, significantly expands our understanding of the molecular composition and properties of soil organic matter (SOM) and its transformation and stabilization processes in various ecosystems and geochemical conditions. The aim of this review is to identify and analyze studies related to the application of nuclear magnetic resonance (NMR) and electron paramagnetic resonance (EPR) spectroscopy techniques to study the molecular composition and transformation of organic matter in virgin and arable soils. This article is mainly based on three research questions: (1) Which NMR spectroscopy techniques are used to study SOM, and what are their disadvantages and advantages? (2) How is the NMR spectroscopy technique used to study the molecular structure of different pools of SOM? (3) How is ESR spectroscopy used in SOM chemistry, and what are its advantages and limitations? Relevant studies published between 1996 and 2024 were searched in four databases: eLIBRARY, MDPI, ScienceDirect and Springer. We excluded non-English-language articles, review articles, non-peer-reviewed articles and other non-article publications, as well as publications that were not available according to the search protocols. Exclusion criteria for articles were studies that used NMR and EPR techniques to study non-SOM and where these techniques were not the primary methods. Our scoping review found that both solid-state and solution-state NMR spectroscopy are commonly used to study the structure of soil organic matter (SOM). Solution-phase NMR is particularly useful for studying soluble SOM components of a low molecular weight, whereas solid-phase NMR offers advantages such as higher ¹³C atom concentration for stronger signals and faster analysis time. However, solution-phase NMR has limitations including sample insolubility, potential signal aggregation and reduced sensitivity and resolution. Solid-state NMR is better at detecting non-protonated carbon atoms and identifying heterogeneous regions within structures. EPR spectroscopy, on the other hand, offers significant advantages in experimental biochemistry due to its high sensitivity and ability to provide detailed information about substances containing free radicals (FRs), aiding in the assessment of their reactivity and transformations. Understanding the FR structure in biopolymers can help to study the formation and transformation of SOM. The integration of two- and three-dimensional NMR spectroscopy with other analytical methods, such as chromatography, mass spectrometry, etc., provides a more comprehensive approach to deciphering the complex composition of SOM than one-dimensional techniques alone. Full article

Although the dopamine hypothesis of schizophrenia explains the effects of all the available antipsychotics in clinical use, there is an increasing need for developing new drugs for the treatment of the positive, negative, and cognitive symptoms of chronic psychoses. Xanomeline–trospium (KarXT) is a drug combination that is based on the essential role played by acetylcholine in the regulation of cognitive processes and the interactions between this neurotransmitter and other signaling pathways in the central nervous system, with a potential role in the onset of schizophrenia, Alzheimer’s disease, and substance use disorders. A systematic literature review that included four electronic databases (PubMed, Cochrane, Clarivate/Web of Science, and Google Scholar) and the US National Library of Medicine database for clinical trials detected twenty-one sources referring to fourteen studies focused on KarXT, out of which only four have available results. Based on the results of these trials, the short-term efficacy and tolerability of xanomeline–trospium are good, but more data are needed before this drug combination may be recommended for clinical use. However, on a theoretical level, the exploration of KarXT is useful for increasing the interest of researchers in finding new, non-dopaminergic, antipsychotics that could be used either as monotherapy or as add-on drugs. Full article

Geopolymer concrete (GPC) serves as a sustainable substitute for conventional concrete by employing alternative cementitious materials such as fly ash (FA) instead of ordinary Portland cement (OPC), contributing to environmental and durability benefits. To increase the rate of utilization of FA in the construction industry, distinctive characteristics of two machine learning (ML) methods, namely, gene expression programming (GEP) and multi-expression programming (MEP), were utilized in this study to propose precise prediction models for the compressive strength and split tensile strength of GPC comprising FA as a binder. A comprehensive database was collated, which comprised 301 compressive strength and 96 split tensile strength results. Seven distinct input variables were employed for the modeling purpose, i.e., FA, sodium hydroxide, sodium silicate, water, superplasticizer, and fine and coarse aggregates contents. The performance of the developed models was assessed via numerous statistical metrics and absolute error plots. In addition, a parametric analysis of the finalized models was performed to validate the prediction ability and accuracy of the finalized models. The GEP-based prediction models exhibited better performance, accuracy, and generalization capability compared with the MEP-based models in this study. The GEP-based models demonstrated higher correlation coefficients (R) for predicting the compressive and split tensile strengths, with the values of 0.89 and 0.87, respectively, compared with the MEP-based models, which yielded the R values of 0.76 and 0.73, respectively. The mean absolute errors for the GEP- and MEP-based models for predicting the compressive strength were 5.09 MPa and 6.78 MPa, respectively, while those for the split tensile strengths were 0.42 MPa and 0.51 MPa, respectively. The finalized models offered simple mathematical formulations using the GEP and Python code-based formulations from MEP for predicting the compressive and tensile strengths of GPC. The developed models indicated practical application potential in optimizing geopolymer mix designs. This research work contributes to the ongoing efforts in advancing ML applications in the construction industry, highlighting the importance of sustainable materials for the future. Full article

African swine fever (ASF) is a deadly disease of swine currently causing a worldwide pandemic, leading to severe economic consequences for the porcine industry. The control of disease spread is hampered by the limitation of available effective vaccines. Live attenuated vaccines (LAVs) are currently the most advanced vaccine prototypes, providing strong protection against ASF. However, the significant advances achieved using LAVs must be complemented with further studies to analyze vaccine-induced immunity. Here, we characterized the onset of cross-protective immunity triggered by the LAV candidate BA71ΔCD2. Intranasally vaccinated pigs were challenged with the virulent Georgia 2007/1 strain at days 3, 7 and 12 postvaccination. Only the animals vaccinated 12 days before the challenge had effectively controlled infection progression, showing low virus loads, minor clinical signs and a lack of the unbalanced inflammatory response characteristic of severe disease. Contrarily, the animals vaccinated 3 or 7 days before the challenge just showed a minor delay in disease progression. An analysis of the humoral response and whole blood transcriptome signatures demonstrated that the control of infection was associated with the presence of virus-specific IgG and a cytotoxic response before the challenge. These results contribute to our understanding of protective immunity induced by LAV-based vaccines, encouraging their use in emergency responses in ASF-affected areas. Full article

Hardness is one of the most crucial mechanical properties, serving as a key indicator of a material’s suitability for specific applications and its resistance to fracturing or deformation under operational conditions. Machine learning techniques have emerged as valuable tools for swiftly and accurately predicting material behavior. In this study, regression methods including decision trees, adaptive boosting, extreme gradient boosting, and random forest were employed to forecast Vickers hardness values based solely on scanned monochromatic images of indentation imprints, eliminating the need for diagonal measurements. The dataset comprised 54 images of D2 steel in various states, including commercial, quenched, tempered, and coated with Titanium Niobium Nitride (TiNbN). Due to the limited number of images, non-deep machine learning techniques were utilized. The Random Forest technique exhibited superior performance, achieving a Root Mean Square Error (RMSE) of 0.95, Mean Absolute Error (MAE) of 0.12, and Coefficient of Determination ($R^2$) ≈ 1, surpassing the other methods considered in this study. These results suggest that employing machine learning algorithms for predicting Vickers hardness from scanned images offers a promising avenue for rapid and accurate material assessment, potentially streamlining quality control processes in industrial settings. Full article

Ovarian cancer (OC) is an umbrella term for cancerous malignancies affecting the ovaries, yet treatment options for all subtypes are predominantly derived from high-grade serous ovarian cancer, the largest subgroup. The concept of "functional precision medicine" involves gaining personalized insights on therapy choice, based on direct exposure of patient tissues to drugs. This especially holds promise for rare subtypes like low-grade serous ovarian cancer (LGSOC). This study aims to establish an in vivo model for LGSOC using zebrafish embryos, comparing treatment responses previously observed in mouse PDX models, cell lines and 3D tumor models. To address this goal, a well-characterized patient-derived LGSOC cell line with the KRAS mutation c.35 G > T (p.(Gly12Val)) was used. Fluorescently labeled tumor cells were injected into the perivitelline space of 2 days’ post-fertilization zebrafish embryos. At 1 day post-injection, xenografts were assessed for tumor size, followed by random allocation into treatment groups with trametinib, luminespib and trametinib + luminespib. Subsequently, xenografts were euthanized and analyzed for apoptosis and proliferation by confocal microscopy. Tumor cells formed compact tumor masses (n = 84) in vivo, with clear Ki67 staining, indicating proliferation. Zebrafish xenografts exhibited sensitivity to trametinib and luminespib, individually or combined, within a two-week period, establishing them as a rapid and complementary tool to existing in vitro and in vivo models for evaluating targeted therapies in LGSOC. Full article

This work studied the influence of the voltage parameters on the friction and superlubricity performances of LiPF₆-based ionic liquids (ILs). The results show that the voltage direction and magnitude greatly affected the friction performances of ILs and that macroscale superlubricity can be achieved with a stimulation of −0.1 V. The surface analysis and experiment results indicate that the voltage magnitude influences the coefficient of friction (COF) by determining the types of substances in the tribochemical film formed on the ball, while the voltage direction influences the COF by affecting the adsorption behavior of Li(PEG)⁺ ions on the ball. At −0.1 V, the cation group Li(PEG)⁺ adsorption film and FeOOH-containing tribochemical film contribute to friction reduction. The formation of Fe_xO_y within the tribochemical film results in an increase in friction at −0.8 V. The limited adsorption of Li(PEG)⁺ ions and the formation of Fe_xO_y contribute to the elevated COF at +0.1 V. This work proves that the friction performances of LiPF₆-based ILs could be affected by voltage parameters. A lubrication model was proposed hoping to provide a basic understanding of the lubrication mechanisms of ILs in the electric environment. Full article

This article analyzes local-level normative regulations aimed at directly or indirectly conserving the urban landscape in rural areas. Using a discursive analysis methodology on regulatory documents being enforced in a series of localities assigned to a tourism promotion program, the evidence suggests that promotional activity retroactively influences the phraseology of these municipal regulations. The results obtained point to the existence of perpetuating historicist approaches within the current regulations, which appear to largely derive from the search for success in the tourism market and the resulting benefits to the local economy. Full article

Coastal areas are fundamental enclaves for economic and recreational development, attracting a large population worldwide. However, these factors have generated significant pressure on the coastal landscape, requiring territorial management strategies to protect and control its degradation. The coastal landscape, composed of abiotic and biotic elements, plays a crucial role in human wellbeing and the conservation of the natural environment. This study focuses on the southeast area of the Ría de Arosa, on the western coast of Galicia, known for its unique geomorphological features such as estuaries. The main objective is to generate high-resolution thematic maps for territorial planning and conservation of the natural and cultural landscape. Using methodologies based on geographic information systems, various factors of the natural environment will be analyzed to obtain objective results, presenting cartography of landscape units, along with quality and fragility landscape maps. In addition, active strategies are proposed such as multiple land uses or the development of geotourism to preserve, exploit, and manage the landscape better. This work contributes to better understanding the vulnerability of the coastal landscape and provides practical tools for its sustainable management in a context of accelerated global change. Full article