Search Results (488)

Gas-insulated transmission lines (GILs) are increasingly deployed in dense urban power networks, where complex construction activities may introduce external mechanical impacts and pose risks to pipeline structural integrity. However, existing GIL monitoring approaches mainly emphasize electrical and gas-state parameters, while lightweight solutions capable of rapidly detecting and localizing impact-induced structural anomalies remain limited. To address this gap, this paper proposes an intelligent end-edge-cloud monitoring system for impact anomaly detection in GIL pipelines. Numerical simulations are first conducted to analyze the dynamic response characteristics of the pipeline under impacts of varying magnitudes, orientations, and locations, revealing the relationship between impact scenarios and vibration mode evolution. An end-tier multi-parameter intelligent sensor is then developed, integrating triaxial acceleration and angular velocity measurement with embedded lightweight computing. Laboratory impact experiments are performed to acquire sensor data, which are used to train and validate a multi-class extreme gradient boosting (XGBoost) model deployed at the edge tier for accurate impact-location identification. Results show that, even with a single sensor positioned at the pipeline midpoint, fusing acceleration and angular velocity features enables reliable discrimination of impact regions. Finally, a lightweight cloud platform is implemented for visualizing structural responses and environmental parameters with downsampled edge-side data. The proposed system achieves rapid sensor-level anomaly detection, precise edge-level localization, and unified cloud-level monitoring, offering a low-cost and easily deployable solution for GIL structural health assessment. Full article

This study proposes an AI-based framework for impact analysis of wooden structures, focusing on quantitatively assessing how individual seismic elements and their spatial locations influence structural response. A single-story residential building was used as a case study. Numerical time-history analyses were performed using a detailed three-dimensional nonlinear model, and parametric variations in stiffness and strength were systematically generated using an orthogonal array. Machine learning models were then trained to investigate the relationship between these parameters and seismic responses, and explainable artificial intelligence (XAI) techniques, including SHAP, were applied to evaluate and interpret parameter influences. The results suggest that wall elements oriented parallel to the target inter-story drift direction generally have the greatest effect on seismic response. Quantitative analysis indicates that the relative importance of these elements roughly corresponds to their wall lengths, providing physically interpretable evidence. Model comparisons show that linear regression achieves high accuracy in the elastic range, while Gradient Boosting performs better under strong excitations inducing nonlinear behavior, reflecting the transition from elastic to plastic response. SHAP-based analysis further provides insights into both the magnitude and direction of parameter influence, enabling element- and location-specific interpretation not readily obtained from traditional global sensitivity measures. Overall, the findings indicate that the proposed framework has the potential to support the identification of influential structural elements and the quantitative assessment of their contributions, which could assist in informed engineering decision-making. Full article

Objective: To evaluate the clinical success outcomes and risk factors associated with dental implants placed with simultaneous bone augmentation in a large-scale, real-world cohort. Methods: A retrospective analysis was conducted on 158,824 implants, including 45,715 Dental Bone Grafts, placed between 2014 and 2022 within a national healthcare network. Multivariate Generalized Estimating Equations were utilized to assess the impact of demographic, anatomical, and procedural variables on implant failure. Results: The augmented cohort demonstrated a high clinical success rate of 97.83% (2.17% failure), statistically comparable to the general implant population. Failures were predominantly early (<1 year), accounting for 70% of losses. Significant independent risk factors included immediate implant placement (3.08% failure vs. 2.07% for delayed), male gender, and maxillary location. Notably, low socioeconomic status (SES) emerged as a significant predictor, with a failure rate of 3.07% compared to 2.06% in high-SES groups. Conclusions: Simultaneous bone augmentation is a predictable modality that does not inherently increase implant failure risk, supporting the stabilization hypothesis. However, failure is modulated by specific variables. The identification of lower SES, male gender, and immediate placement as significant risk indicators highlights the necessity for personalized risk assessment and targeted protocols to optimize outcomes in augmented sites. Full article

Based on the collected mobile phone location data, this paper analyzes changes in four mobile location-based indicators and their spatiotemporal distribution characteristics before and after the earthquake, summarizing crowd movement patterns and communication behaviors after the M_S6.8 Dingri earthquake. By comparing natural neighbor interpolation and Thiessen polygon interpolation methods, we explore novel rapid assessment approaches for earthquake perception ranges, combined with actual seismic intensity maps. The results indicate an uneven distribution of population and differing dynamics in mobile phone signal activity. This reflects different behavioral patterns and the potential perceived extent of the earthquake. Within 50 km of the epicenter, all four indicators showed varying degrees of decline post-earthquake, while areas beyond 100 km exhibited short-term surges, reflecting differentiated behavioral responses based on seismic impact severity. In areas experiencing strong shaking, risk avoidance behavior predominated, while in areas where shaking was noticeable but less severe, communication behavior was more prominent. Mobile data decline zones showed high spatial correlation with intensity VIII+ regions, proving their effectiveness as rapid indicators for identifying strongly affected areas. Notably, mobile location data enabled accurate identification of strongly affected zones within 30 min post-earthquake. Full article

The deployment of wireless seismic nodal systems necessitates the efficient identification of optimal locations for sensor installation, considering factors such as ground stability and the absence of interference. Semantic segmentation of satellite imagery has advanced significantly, and its application to this specific task remains unexplored. This work presents a baseline empirical evaluation of the U-Net architecture for the semantic segmentation of surfaces applicable for seismic sensor installation. We utilize a novel dataset of Sentinel-2 multispectral images, specifically labeled for this purpose. The study investigates the impact of pretrained encoders (EfficientNetB2, Cross-Stage Partial Darknet53—CSPDarknet53, and Multi-Axis Vision Transformer—MAxViT), different combinations of Sentinel-2 spectral bands (Red, Green, Blue (RGB), RGB+Near Infrared (NIR), 10-bands with 10 and 20 m/pix spatial resolution, full 13-band), and a technique for improving small object segmentation by modifying the input convolutional layer stride. Experimental results demonstrate that the CSPDarknet53 encoder generally outperforms the others (IoU = 0.534, Precision = 0.716, Recall = 0.635). The combination of RGB and Near-Infrared bands (10 m/pixel resolution) yielded the most robust performance across most configurations. Reducing the input stride from 2 to 1 proved beneficial for segmenting small linear objects like roads. The findings establish a baseline for this novel task and provide practical insights for optimizing deep learning models in the context of automated seismic nodal network installation planning. Full article

This study investigates the traditional life within Al-Gh’deer Market Square, which constitutes a fundamental component of the vernacular urban fabric of Sidi Okba’s old city from a sensorial perspective. This oasis, located in the southeast of Algeria, is currently severely degraded and requires urban and architectural preservation. However, the sensory experiences that once characterised traditional urban life have not yet been systematically explored. The aim of this study is to fill this gap by analysing the historical atmospheres depicted in various literary and iconographic sources created by French and European explorers who visited Algeria during the colonial period. This research highlights each component of the “Al-Gh’deer” market square, which had a sensory impact on writers and photographers during their visit to Sidi Okba. This impact is revealed through the different tangible and intangible signals generated by these components, which were then felt and described textually and/or visually by the travellers. To this end, the thematic content analysis is used as a research technique in order to analyse this textual corpus, whilst the image formatting and staging constitute the method used for the iconographic corpus study. The first method makes it possible to detect the most relayed ambiences by travellers. This is revealed by the identification and computation of the associated words and/or expressions within the considered textual corpus. The second technique consists of the extraction of the elements generating the physical signals that should create a sensory relationship with the people within the scene or looking at it. The identified ambiences among the two corpora are crossed in order to determine the most felt ones in the marketplace as well as the various components generating them. The outcomes of this research work would serve as a basis for revitalisation initiatives within the frame of socio-economic and cultural development projects. Full article

Strength and the permeability coefficient are recognized as the two main design parameters for permeable concrete. Although adding an appropriate amount of nano-silica (NS) can enhance the slurry strength and enhance the bond between the aggregate and cementitious material, research on the combined effects of porosity and NS on the behavior of permeable concrete is limited. An experimental program was carried out to demonstrate the impact of NS on the permeability (K) and strength (f_c) of permeable concrete. The tested variables included the NS content (0, 0.5, 1.0, 1.5, 2.0, and 2.5%) and the porosity (p = 15, 20, and 25%), following the identification of an optimal water-to-binder (w/b) ratio of 0.3. It was found that the addition of NS alters the failure mechanism by transferring the critical failure location from the cementitious matrix to aggregate particles. An additive of 1% NS shows the most significant enhancement in the concrete strength, with improvement efficacy increasing substantially with the porosity. Specifically, the 28-day strength of permeable concrete modified with 1% NS increased by 6.4%, 16.1%, and 38.5% for mixes with 15%, 20%, and 25% porosity, respectively. Meanwhile, NS improves the permeability with 0.5% dosage, providing the most effective enhancement. Finally, an empirical expression between permeability and porosity was developed based on the test results, which allows engineers to calculate the required porosity (e.g., p ≈ 17% for K = 1.0 cm/s) to meet specific permeability in pavement applications. Full article

Forested areas in Poland comprise numerous post-mining sites that hinder effective forest management. Such mining remnants may pose a threat to humans, animals, and operating forest machines. This study aimed to determine the feasibility of inventorying such man-made landforms as mining waste heaps, excavations, remnants of shallow shafts, adits, etc., using the Digital Elevation Model (DEM) based on Airborne Laser Scanning (ALS) data provided by the national agency (the Head Office of Geodesy and Cartography—HOGC) as open data. The DEM, when combined with other cartographic materials using GIS, accurately reflects the anthropogenic transformation evident in the topography. This paper presents the results of inventorying remnants of iron ore mining in the present-day forested area located between Krzepice, Kłobuck, and Częstochowa in southern Poland. The identification and inventory of post-mining landforms, mainly mounds resulting from shallow shaft mining operations, were supplemented by their digitization, automatically providing information on parameters such as perimeter (ranged in most cases from 24.3 to 159 m), surface area (46.9 to 1656 m²), length and width (7.8 to 59.2 m). The heights of the investigated structures were also read from the DEM, ranging from 0.3 to 4.1 m. Much larger structures were also identified, but they occurred accidentally (up to 23.5 m in height). In this manner, approximately 823 morphological forms were characterized, resulting in a database. Test fieldwork was then conducted to verify the DEM readings. It was proposed to calculate deformation indexes (Id [%]) for forested areas and apply them when estimating the forest management hindrance index used by the State Forests. The studied forest compartments managed by State Forests were characterized by an Id value from 0.1 to 55.5%. This type of measure provides a helpful tool in planning forestry operations in areas with diverse topography, including those transformed by mining activities. The actual environmental impact is highlighted. Forest management practices in the study area must take into consideration, in particular, topography, as well as geology and hydrology. Studies have shown that the DEM based on the ALS data is sufficiently accurate to detect even minor post-mining deformations (which may be important, in particular, in inaccessible areas). The recorded parameters can be considered when planning management, protection interventions, or reclamation activities. Full article

(1) Background: Ecuador is the fourth largest milk producer in Latin America, where ap-proximately 80% of production originates from small family farms located in the Andean region. Despite their socioeconomic importance, these farms face challenges related to low technical efficiency. While there are specific studies on efficiency in dairy systems from other regions, a knowledge gap persists regarding the temporal evolution of technical efficiency (TE) in Ecuadorian Andean dairy farms, especially during crisis periods such as the COVID-19 pandemic. The objective of this study was to evaluate the evolution of TE of family dairy farms in the Ecuadorian Andean region during the period 2018–2024 and to analyze the impact of the pandemic on said efficiency. (2) Methods: Data Envelopment Analysis (DEA) with input orientation and bootstrap simulation was employed to estimate TE, using data from a representative sample that included between 2370 and 2987 farms per year (approximately 25% of the national database of the Ministry of Agriculture and Livestock). Farms were selected based on the availability of complete information on key variables: number of milking cows, area dedicated to forage, family and hired labor (annual hours), and total annual milk production. Statistical analysis included ANOVA to compare mean TE values between years, post-hoc tests to identify specific differences between periods, and the identification of factors related to the TE. (3) Results: The mean TE of Andean dairy farms increased significantly from 0.37 in 2018 to 0.44 in 2024 (p < 0.10), evidencing sustained improvement, although the mean is still distant from the efficiency frontier. The analysis revealed a notable decrease in TE during 2020–2021, coinciding with the period of greatest impact of the COVID-19 pandemic, followed by progressive recovery in subsequent years. The TE distribution showed that between 70% and 75% of farms remained below 0.50 throughout the analyzed period, while only 8–12% achieved levels above 0.70. The main sources of technical inefficiency identified were relative excesses of labor and forage area in relation to milk production obtained. When compared with international studies, Ecuadorian farms present TE levels substantially lower than those reported in the European Union (>0.80) and similar to or slightly lower than those found in Turkey (0.61–0.71). (4) Conclusions: Family dairy farms in the Ecuadorian Andean region operate with technical efficiency levels considerably below their potential and international standards, suggesting substantial scope for improvement through the optimization of productive resource use, particularly labor and land. The COVID-19 pandemic impacted the sector’s efficiency negatively but temporarily, demonstrating resilience and recovery capacity. These findings are relevant to the design of public policies and technical assistance programs aimed at sustainable intensification of family dairy production in the Andes, with an emphasis on improving labor productivity and the efficient use of forage area. Full article

This paper explores the attitudes and involvement of tourism development in rural and remote cross-border areas in Croatia and Slovenia. These locations were selected due to their valuable cultural, historical, and natural resources. The purpose of this study was to identify how tourism can contribute to the sustainable development of these areas through the identification of positive and negative impacts based on perceptions obtained through a survey of residents and visitors. Results found that the local population positively assessed employment and quality of life as benefits generated by tourism, while identifying negative consequences such as price increases and crowds. Visitors highlighted negative aspects (environmental impact, availability of services), while recognizing the contribution of tourism to the preservation of space. The results emphasize the importance of harmonizing the interests of local communities and visitors in the planning of sustainable tourism, through continuous involvement of local stakeholders, periodic monitoring of attitudes, and preservation of natural and cultural resources as the basis of tourism development. Because tourism can change the area and culture of any destination, it is important to measure key stakeholder attitudes specific to tourism development in cross-border rural areas. Full article

Background: Milk is a highly nutritious food, but its composition makes it an ideal medium for microbial growth, particularly for bacteria like Staphylococcus aureus (S. aureus). In Ecuador, raw milk consumption is culturally rooted, and contamination risks are heightened, especially in informal markets. Staphylococcus aureus, a Gram-positive, coagulase-positive bacterium, commonly colonizes mucous membranes and can cause a range of infections due to its production of thermostable toxins. Its impact extends to bovine mastitis, severely affecting dairy production. Of particular concern is the emergence of methicillin-resistant S. aureus (MRSA) strains, associated with the acquisition of the mecA gene located on the “staphylococcal chromosomal cassette mec” (SCCmec) element and identification of a mecA homologue, mecC, further complicates detection and monitoring efforts. Objectives: This study evaluated the prevalence of S. aureus and MRSA strains in raw milk from Ecuadorian provinces Pichincha and Manabí. Methods: A total of 633 samples were collected and analyzed via real-time PCR (qPCR) and bacterial isolation methods, complemented by endpoint PCR assays for mecA and mecC genes detection. Results: A high prevalence of S. aureus (84%) was observed, with significant differences between regions. MRSA was detected in 23% of all samples, with mecA being more prevalent than mecC among isolates. Sequencing of 16S rDNA confirmed the identity of isolates, while phylogenetic analysis of mecA and mecC genes validated their presence. The findings suggest that suboptimal hygiene practices and varied biosecurity protocols, especially among small and medium dairy producers, may contribute to the persistence of resistant strains. Conclusions: This study highlights the presence of S. aureus and MRSA in raw milk, underscoring the need for strengthened surveillance, improved hygiene practices, the use of molecular diagnostic tools, and proper heat treatments to reduce the public health risks associated with contaminated milk and its derivatives. Full article

Urban agriculture’s potential for food production and other social benefits is widely documented. However, the diversity of organisational structures and contextual factors that shape and drive the practice leads to a range of productivity levels. Yet, most studies estimate productivity using average production data, which compromises the reliability of the estimates. The objective of the study presented here is to develop a GIS-based spatial analytical framework that takes into account varying levels of productivity for four urban food garden types: Home, Community, Educational, and Commercial. We apply this analytical framework in Bogotá, Colombia, a city at the forefront of policies promoting urban agriculture, where we collected data from a sample of urban food gardens (i.e., produce yield, resource use, and social benefits). To increase the precision and reliability of the estimates, we perform a spatial Multi-Criteria Decision Analysis through several ArcGIS pro 3.1 functions. This allows the identification of suitable areas for each urban agriculture type, based on key spatial and social characteristics (location, proximity to roads and to rivers, private or public land, urban density, and socio-economic demographic conditions). Results suggest that 25% of Bogotá’s surface area (including vacant urban land and roofs) presents potential physical and social conditions for food growing, within which Home Gardens occupy the largest share of suitable land. This shows that land availability is not a key limiting factor to a possible expansion of urban agriculture, particularly at a household level. Resource consumption and educational benefits are also estimated, hence providing a comprehensive picture of the impact of urban food production at a city scale. Full article

Toxoplasma gondii, a widespread parasite, poses significant public health concerns. It infects humans and animals, with rodents serving as important intermediate hosts. The present study investigated the prevalence and genetic ITS1 diversity of T. gondii in wild rodents from Lithuania. A total of 469 rodents from eight species were captured across various regions, and DNA from neural and cardiac tissues was analyzed using nested PCR. Overall prevalence of T. gondii was 26.2% (95% CI = 22.3–30.5). The prevalence of infection varied among rodent species (0–50.0%) and across geographic locations. A mere few rodents exhibited concurrent infections in both tissues examined. Toxoplasma gondii was detected more frequently in the brains of Apodemus flavicollis and hearts of Clethrionomys glareolus, and in the males of Microtus arvalis. A total of 19 distinct ITS1 genotypes were identified, including 17 novel ones; Genotype 1 was the most prevalent and widely distributed. Phylogenetic and network analyses revealed a star-like topology centered on Genotype 1 and confirmed the accurate identification of T. gondii in Lithuanian rodents. This study provides the first evidence of T. gondii in wild rodents in Lithuania, highlighting the need for further research on its prevalence and potential impact on public health and wildlife. Full article

Our research aims to provide a comparative analysis of water governance components by presenting the complexity of water-covered land use by the extractive industry in terms of legal, economic, and environmental protection aspects in Poland and Malaysia, along with the corresponding regulations and their implications. This paper discusses the legal forms of land ownership and use, as well as the currently applied principles for calculating fees for using state-owned water covered land that contains mineral deposits. We also present a comparison of selected technologies for the extraction of sand and gravel aggregates under water with their environmental impact. This research highlights the need for specialized valuation frameworks tailored to the geological and regulatory landscape of Poland and Malaysia. We suggest that the market value of land located above a mineral deposit, calculated individually for each deposit-property, should serve as the basis for calculating the lease fee. This discussion should encompass not only the principles and methodology involved in estimating the magnitudes of lease rents on mining industry and its profitability, but also the identification and criteria for assessing the risks associated with ongoing or planned mining ventures and concerns about the protection of river ecosystems. Our research contributes in providing data to stakeholders on extractive industry that operates within flowing and standing inland waters. The key finding of our research is that, in our opinion, the water governance frameworks in Poland and Malaysia are inadequate for protecting public finances and for internalizing the environmental externalities inherent in the economics of mining. Full article

Urbanization has led to significant alterations in river morphology and ecological function, highlighting the need for effective tools to assess and manage these changes. Traditional hydromorphological evaluation methods often fail to capture complex relationships between physical habitat features and anthropogenic pressures. The aim of this study was to apply the River Habitat Survey (RHS) method and Interactive Activation and Competition (IAC) artificial neural networks to assess and describe the hydromorphological condition of the Jasień and Olechówka rivers, located in an urbanized area. The RHS method enables the evaluation of the physical characteristics of rivers and the impact of anthropogenic activities on their environment, in accordance with the requirements of the Water Framework Directive (WFD). Field surveys documented features such as bank structure, vegetation, channel substrate, and artificial modifications. In the subsequent phase of the study, an IAC-type neural network was employed to analyze and interpret the RHS data. This network architecture allows for the identification of hidden relationships between variables, the completion of missing data, as well as contextual analysis and generalization based on similar cases. Integrating RHS data with IAC analysis enabled the development of a model supporting the assessment of anthropogenic impacts on the hydromorphological condition of rivers. The results indicate that both rivers exhibit a high degree of modification, particularly within urban sections, which adversely affects their retention capacity and ecological function. This combined methodological approach provides an innovative and flexible tool for supporting urban river restoration and flood risk management, addressing some of the limitations of existing assessment techniques. Full article