Search Results (107)

Wireless sensor networks (WSNs) are used for data acquisition and transmission in unmanned surface vessels (USVs). However, the openness of wireless networks makes USVs highly susceptible to false data injection (FDI) attacks during data transmission, which affects the sensors’ ability to receive real data and leads to decision-making errors in the control center. In this paper, a novel dynamic data encryption method is proposed whereby data are encrypted prior to transmission and the key is dynamically updated using historical system data, with a view to increasing the difficulty for attackers to crack the ciphertext. At the same time, a dynamic relationship is established among ciphertext, key, and auxiliary encrypted ciphertext, and an attack detection scheme based on dynamic encryption is designed to realize instant detection and localization of FDI attacks. Further, an $H_{\infty }$ fusion filter is designed to filter external interference noise, and the real information is estimated or restored by the weighted fusion algorithm. Ultimately, the validity of the proposed scheme is confirmed through simulation experiments. Full article

Corporate environmental claims often neglect the substantial ecological impact of land-use changes. This case study examines the spatial dimension of retail-driven land-use transformation by analyzing supermarket expansion in the Veneto region (northern Italy), with a focus on a large grocery retailer. We evaluated its corporate environmental claims by assessing land consumption patterns from 1983 to 2024 using Geographic Information Systems (GIS). The GIS-based methodology involved geocoding 113 Points of Sale (POS—individual retail outlets), performing photo-interpretation of historical aerial imagery, and classifying land-cover types prior to construction. We applied spatial metrics such as total converted surface area, land-cover class frequency across eight categories (e.g., agricultural, herbaceous, arboreal), and the average linear distance between afforestation sites and POS developed on previously rural land. Our findings reveal that 65.97% of the total land converted for Points of Sale development occurred in rural areas, primarily agricultural and herbaceous lands. These landscapes play a critical role in supporting urban biodiversity and providing essential ecosystem services, which are increasingly threatened by unchecked land conversion. While the corporate sustainability reports and marketing strategies emphasize afforestation efforts under their “We Love Nature” initiative, our spatial analysis uncovers no evidence of actual land-use conversion. Additionally, reforestation activities are located an average of 40.75 km from converted sites, undermining their role as effective compensatory measures. These findings raise concerns about selective disclosure and greenwashing, driving the need for more comprehensive and transparent corporate sustainability reporting. The study argues for stronger policy frameworks to incentivize urban regeneration over greenfield development and calls for the integration of land-use data into corporate sustainability disclosures. By combining geospatial methods with content analysis, the research offers new insights into the intersection of land use, business practices, and environmental sustainability in climate-vulnerable regions. Full article

Banana is an important cash and food crop worldwide. Recent outbreaks of banana diseases are threatening the global banana industry and smallholder livelihoods. Remote sensing data offer the potential to detect the presence of disease, but formal analysis is needed to compare inferred disease data with observed disease data. In this study, we present a novel remote-sensing-based framework that combines Landsat-8 imagery with meteorology-informed phenological models and machine learning to identify anomalies in banana crop health. Unlike prior studies, our approach integrates domain-specific crop phenology to enhance the specificity of anomaly detection. We used a pixel-level random forest (RF) model to predict 11 key vegetation indices (VIs) as a function of historical meteorological conditions, specifically daytime and nighttime temperature from MODIS and precipitation from NASA GES DISC. By training on periods of healthy crop growth, the RF model establishes expected VI values under disease-free conditions. Disease presence is then detected by quantifying the deviations between observed VIs from Landsat-8 imagery and these predicted healthy VI values. The model demonstrated robust predictive reliability in accounting for seasonal variations, with forecasting errors for all VIs remaining within 10% when applied to a disease-free control plantation. Applied to two documented outbreak cases, the results show strong spatial alignment between flagged anomalies and historical reports of banana bunchy top disease (BBTD) and Fusarium wilt Tropical Race 4 (TR4). Specifically, for BBTD in Australia, a strong correlation of 0.73 was observed between infection counts and the discrepancy between predicted and observed NDVI values at the pixel with the highest number of infections. Notably, VI declines preceded reported infection rises by approximately two months. For TR4 in Mozambique, the approach successfully tracked disease progression, revealing clear spatial spread patterns and correlations as high as 0.98 between VI anomalies and disease cases in some pixels. These findings support the potential of our method as a scalable early warning system for banana disease detection. Full article

This paper examines arbitrage opportunities available in one of the largest wholesale electricity markets in the world, the Midcontinent Independent System Operator (MISO) electricity exchange. While prior research suggests that market efficiency on the exchange has increased over time, this study reveals that historical pricing information can still be used to generate positive returns. I find that a trading rule based on prior spot and forward prices generates statistically and economically significant risk-adjusted returns across the entire MISO footprint. These returns may in part be explained by the relatively small number of financial traders in the market and the ability of generation owners to exercise market power. Full article

In the United States, the Food and Drug Administration (FDA) is the regulatory authority with the responsibility to evaluate scientific data included in each vaccine’s prescribing information (e.g., safety, indication(s) for use, and dosing schedule) based on several factors, including safety, quality, potency, and effectiveness in preventing disease to assess benefit/risk prior to approval. After approval, the FDA continues to work with sponsors to ensure safety and effectiveness data in the prescribing information remain current. In conjunction with FDA approval or authorization, the Advisory Committee on Immunization Practices (ACIP) recommends immunization dosing schedules and target populations for use. ACIP recommendations that are adopted by the Centers for Disease Control and Prevention (CDC) Director inform national immunization schedules, which influence immunization access, coverage, and provider behavior. This targeted review aims to explore historical instances when vaccine dosing regimens approved by the FDA differ from those recommended by the ACIP, focusing on the frequency and factors behind these differences to inform future ACIP recommendations. Out of n = 78 vaccines assessed, the analysis identified n = 5 vaccines with deviations and only one that reduced dosing. Deviations from the FDA label were determined to be a rare occurrence and are most frequently observed to be additive, not reductive. Full article

Background: Desmoid fibromatosis (DF) is a rare, locally aggressive soft tissue tumour with unpredictable clinical behaviour. Historically, treatment has involved surgery; however, contemporary guidelines, such as those from the Desmoid Tumour Working Group, advocate active surveillance. This article reviews current perspectives on DF, focusing on epidemiology, pathogenesis, treatment strategies, emerging research directions and cost effectiveness based on our experience at the West of Scotland Musculoskeletal Oncology Service, Glasgow Royal Infirmary (GRI). Methodology: We reviewed 101 patients diagnosed with desmoid fibromatosis between 2010 and 2024. A review of patient records was conducted to gather information on demographics, date of diagnosis, prior treatment, treatment initiation, intervention types, imaging intervals, follow-up duration, recurrence rate for surgery and other intervention, and discharge timelines. All data was systematically organized and analyzed to assess our outcomes. Results: Out of 101 patients with DF in the study, 66% were females. The most common site of primary tumour was lower extremity (39.6%) followed by near equal distribution in upper extremity and trunk. Out of the total cases, 72 (71.2%) were successfully managed with active surveillance involving serial imaging and clinical reviews in accordance with European guidelines. A total of 22 patients (21%) received treatment: 10 underwent surgery alone, 2 had surgery combined with radiotherapy, 8 received only radiotherapy, 1 was treated with hormonal therapy and 1 participated in a trial with Nirogacestat. Of the seven remaining patients, six had unplanned surgery outside followed by active surveillance at GRI. One patient was on alternative treatment modality, homeopathy. The average number of MRI scans per patient was 3.11, with many patients requiring significantly more imaging. MRI surveillance varies significantly in desmoid tumours due to their heterogeneous behaviour. Active or symptomatic tumours often require more frequent scans (every 3–6 months), while stable cases may need only imaging annually or just clinical monitoring. Recurrence was noted in eight patients, all of which were related to prior surgery. The total combined cost of imaging and appointments exceeds £6500 per patient in active surveillance. Conclusions: We conclude that most patients with desmoid fibromatosis in our cohort were effectively treated with active surveillance, consistent with current European guidelines. Surgical management of desmoid fibromatosis in our cohort is historic and has shown a significant recurrence risk. Our study proposes a revised follow-up protocol that significantly reduces costs without compromising on patient care. We suggest a two-year surveillance period for stable disease with patient-initiated return to reduce unnecessary clinic visits, imaging and healthcare costs. Full article

The cold-start problem remains a significant challenge in recommendation systems, particularly in scenarios involving new users or items with insufficient historical interaction data, which severely limits the effectiveness of personalised recommendations. Despite extensive research efforts dedicated to addressing this issue, existing meta-learning approaches, while promising, often rely on the assumption that prior knowledge can be globally shared across all users. This assumption overlooks the inherent inefficiency of information sharing due to diverse user interests, frequently resulting in suboptimal solutions and constrained model performance. To address this limitation, we propose an enhanced meta-learning framework that leverages graph community detection algorithms to cluster users, enabling the extraction of unique prior knowledge within each cluster. This knowledge is then shared efficiently among users with similar interests within the same cluster. Through comparative experiments on cold-start recommendation tasks, our proposed model demonstrates superior performance over traditional methods, validating its effectiveness in improving cold-start recommendation accuracy. Furthermore, this study highlights potential application scenarios and future research directions for advancing cold-start solutions in recommendation systems. Full article

To effectively mitigate service slope disaster risks in mountainous areas and enhance the overall safety of highway operations, based on the geological and structural characteristics of slopes, considering slope technical conditions, overall stability, and potential disaster consequences, 25 important influencing factors are systematically identified. The identification process integrates insights from the relevant literature, expert opinions, and historical disaster maintenance records of such slopes. An integrated approach combining Interpretive Structural Modeling (ISM) and Bayesian Networks (BNs) is utilized to conduct a quantitative analysis of the interrelationships and impact strength of factors influencing the disaster risk of mountainous service highway slopes. The aim is to reveal the causal mechanism of slope disaster risk and provide a scientific basis for risk assessment and prevention strategies. Firstly, the relationship matrix is constructed based on the relevant prior knowledge. Then, the reachability matrix is computed and partitioned into different levels to form a directed graph from which the Bayesian network structure is constructed. Subsequently, the expert’s subjective judgment is further transformed into a set of prior and conditional probabilities embedded in the BN to perform causal inference to predict the probability of risk occurrence. Real-time diagnosis of disaster risk triggers operating slopes using backward reasoning, sensitivity analysis, and strength of influence analysis capabilities. As an example, the earth excavation slope in the mountainous area of Anhui Province is analyzed using the established model. The results showed that the constructed slope failure risk model for mountainous operating highways has good applicability, and the possibility of medium slope failure risk is high with a probability of 34%, where engineering geological conditions, micro-topographic landforms, and the lowest monthly average temperature are the main influencing factors of slope hazard risk for them. The study not only helps deepen the understanding of the evolutionary mechanisms of slope disaster risk but also provides theoretical support and practical guidance for the safe operation and disaster prevention of mountainous highways. The model offers clear risk information, serving as a scientific basis for managing service slope disaster risks. Consequently, it effectively reduces the likelihood of slope disasters and enhances the safety of highway operation. Full article

Evapotranspiration (ET) rates will be affected by climate change and increasing frequency of extreme heat events. To understand how forests may respond to probable future climate conditions, it may be helpful to look at the past relationship between climate and ET. This can be accomplished using satellite imagery since the 1980s, but prior to that, a different approach is required. Using a global ET dataset (1982 to 2010) with 1 km resolution, climate station information from 1850 to 2010, and 54 tree-ring plots from the International Tree-Ring Data Bank (ITRDB) database, ET reconstructions were developed for each vegetated pixel with point-by-point regressions in British Columbia. ET was estimated for the province of British Columbia in Canada from 1850 to 1981, using random forest, support vector machine, and convolutional neural network regressions. ET satellite images from 1982 to 2010 formed our dataset to train models for each vegetated pixel. The random forest regression outperformed the other approaches with lower errors and better robustness (adjusted R² value = 0.69; root mean square error = 10.72 mm/month). Modeled findings indicate that ET rates are generally increasing in British Columbia (ET = 0.0064 × Year + 52.339), but there were regional effects on local ET, as only the Humid Temperate ecodomain had strong correlations of ET with mean summer temperature (r = 0.257, p < 0.01) and mean summer precipitation (r = −0.208, p < 0.05). These historical estimates provide an opportunity to observe spatiotemporal variation in ET across British Columbia and elsewhere. Full article

The UNESCO World Heritage Centre announced in 2011 that the Wadi Rum Protected Area (WRPA) is a global landmark for natural and cultural attraction, which represents an emerging industrial suburban and a critical socio-economic significance to the country of Jordan. The study area in Wadi Rum is located northeast of the Gulf of Aqaba between the African and Arabian plates. The region is historically characterized by significant tectonic activity and seismic events. This study focuses on characterizing the subsurface structural features of Wadi Rum through the application of the geophysical method of controlled-source audio-frequency magnetotellurics (CSAMT). CSAMT data were collected from 16 sounding stations, processed, and qualitatively interpreted. The qualitative interpretation involved two main approaches: constructing sounding curves for each station and generating apparent resistivity maps at fixed depths (frequencies). The results revealed the presence of at least four distinct subsurface layers. The surface layer exhibited relatively low resistivity values (<200 Ω·m), corresponding to alluvial and wadi sediments, as well as mud flats. Two intermediate layers were identified: the first showed very low resistivity values (80–100 Ω·m), likely due to medium-grained bedded sandstone, while the second displayed intermediate resistivity values (100–800 Ω·m), representing coarse basal conglomerates and coarse sandstone formations. The deepest layer demonstrated very high resistivity values (>1000 Ω·m), which were likely attributed to basement rocks. Analysis of resistivity maps, combined with prior geological information, indicates that the subsurface in the study area features a graben-like structure, characterized by two detected faults trending in the northeast (NE) and southwest (SW) directions. The findings of this study, by providing critical insights into the subsurface structure, make a considerable contribution to the urban sustainability of the region, which is necessary for the careful assessment of potential hazards and the strategic planning of future infrastructure development within the protected area. Full article

Traffic speed prediction is difficult because of the complicated dynamic spatiotemporal correlations. Recent studies in spatiotemporal models have achieved impressive outcomes for traffic speed prediction. But many studies use graphs in graph convolutional networks to learn spatial features that are often static. Additionally, effectively modeling long-range temporal features is crucial for prediction accuracy. In order to overcome these challenges, a Spatial–Temporal Adaptive Graph Convolutional Network with Transformer (ST_AGCNT) is designed in this paper. Specifically, an adaptive graph convolution network (AGCN) is designed to extract spatial dependency. An adaptive graph that fuses predefined matrices and learnable matrix is proposed to learn the correlations between nodes. The predefined matrices provide the model with richer prior information, while the learnable matrix can extract the dynamic nature of the nodes. And a temporal transformer (TT) is proposed to extract the long-range temporal dependency. In addition, to learn more information to achieve better results, different historical segments are modeled. Experiments conducted on a real-world traffic dataset confirm the effectiveness of the proposed model when compared to other baseline models. This model demonstrated excellent performance in prediction tasks across different time steps, effectively accomplishing traffic speed forecasting. It provides data support for improving traffic efficiency and reducing resource waste, contributing to the sustainable development of traffic management. Full article

The Australian Bureau of Meteorology provides flood forecasting and warning services across Australia for most major rivers in Australia, in cooperation with other government, local agencies and emergency services. As part of this service, the Bureau issues a flood watch product to provide early advice on a developing situation that may lead to flooding up to 4 days prior to an event. This service is based on (a) an ensemble of available Numerical Weather Prediction (NWP) rainfall forecasts, (b) antecedent soil moisture, stream and dam conditions, (c) hydrological forecasts using event-based models and (d) expert meteorological and hydrological input by Bureau of Meteorology staff, to estimate the risk of reaching pre-specified river height thresholds at locations across the continent. A flood watch provides information about a developing weather situation including forecasting rainfall totals, catchments at risk of flooding, and indicative severity where required. Although there is uncertainty attached to a flood watch, its early dissemination can help individuals and communities to be better prepared should flooding eventuate. This paper investigates the utility of forecasts of daily gridded national runoff to inform the risk of riverine flooding up to 7 days in advance. The gridded national water balance model (AWRA-L) runoff outputs generated using post-processed 9-day Numerical Weather Prediction hindcasts were evaluated as to whether they could accurately predict exceedance probabilities of runoff at gauged locations. The approach was trialed over 75 forecast locations across North East Australia (Queensland). Forecast 3-, 5- and 7-day accumulations of runoff over the catchment corresponding to each location were produced, identifying whether accumulated runoff reached either 95% or 99% historical levels (analogous to minor, moderate and major threshold levels). The performance of AWRA-L runoff-based flood likelihood was benchmarked against that based on precipitation only (i.e., not rainfall–runoff transformation). Both products were evaluated against the observed runoff data measured at the site. Our analysis confirmed that this runoff-based flood likelihood guidance could be used to support the generation of flood watch products. Full article

This study integrates Sustainable Energy Development (SED) with an Energy Ecosystems (EE) framework in Greece to reveal how macrolevel policies, mesolevel infrastructures, and microlevel behaviors shape energy transitions. Drawing on historical data primarily spanning 2010–2024, supplemented by 16 semi-structured expert interviews and a macro–meso–micro analytical approach, it examines SED dimensions—affordability, supply, consumption, and security—within the supplier–producer–distributor–consumer nexus. The findings show notable progress in solar and wind adoption but also underscore persistent challenges such as high import dependency, regulatory inefficiencies, and infrastructural gaps. By proposing targeted policy directions and suggesting a new modus operandi of local-level institutional coordination, the research illustrates how an SED–EE synergy can foster resilience, innovation, and social equity, thereby informing sustainable energy strategies not just for Greece but also for other regions facing similar structural hurdles. The novel integrative perspective of this paper, unlike prior approaches that address either macropolicy targets or microlevel entrepreneurial activity alone, clarifies how mesolevel dynamics facilitate or hamper SED goals. This theoretical and practical synthesis is expected to inform the design of more resilient, equitable, and innovation-driven energy policies. Full article

Accurate prediction of regional wind power generation intervals is an effective support tool for the economic and stable operation of provincial power grid. However, it involves a large amount of high-dimensional meteorological and historical power generation information related to massive wind power stations in a province. In this paper, a lightweight model is developed to directly obtain probabilistic predictions in the form of intervals. Firstly, the input features are formed through a fused image generation method of geographic and meteorological information as well as a power aggregation strategy, which avoids the extensive and tedious data processing process prior to modeling in the traditional approach. Then, in order to effectively consider the spatial meteorological distribution characteristics of regional power stations and the temporal characteristics of historical power, a parallel prediction network architecture of a convolutional neural network (CNN) and long short-term memory (LSTM) is designed. Meanwhile, an efficient channel attention (ECA) mechanism and an improved quantile regression-based loss function are introduced in the training to directly generate prediction intervals. The case study shows that the model proposed in this paper improves the interval prediction performance by at least 12.3% and reduces the deterministic prediction root mean square error (RMSE) by at least 19.4% relative to the benchmark model. Full article

Cultural heritage (CH) sites and monuments share significant historical and cultural value, but at the same time, these are highly vulnerable to deterioration due to age, construction methods, and materials used. Therefore, stability studies for CH structures through numerical analyses allow researchers and stakeholders to safeguard them against time and exposure to hazards. To obtain reliable results for stability studies, detailed and accurate geometric documentation is needed prior to any modeling or simulation. In this context, geomatics technologies like LiDAR and photogrammetry can offer great support in documenting their structural integrity, providing efficient, non-invasive data collection methods that generate 3D point clouds. Nevertheless, despite the benefits, geomatic methods remain underutilized in structural engineering due to limitations in converting 3D point clouds directly for use in finite element modeling (FEM) analysis. The paper aims to review current approaches for the generation of FE models for structural analysis employing data obtained from 3D digital surveys. Each approach is described in detail, providing examples from literature and highlighting its advantages and disadvantages. Studies show that analysis accuracy depends strongly on point cloud level of detail, underlining the importance of precise geomatic surveys. Emerging workflows and semi-automated methods enable point clouds to be integrated with BIM (building information modeling) and FEM, thereby enhancing the contribution that laser scanning techniques and 3D modeling provide for the analysis of the stability of structures belonging to cultural heritage. Full article