Search Results (127,978)

In wireless sensor networks (WSNs), establishing a robust key agreement is essential for securing communications. Various performance metrics are typically employed to evaluate these schemes, including storage requirements, communication overhead, and computational costs. Group key establishment ensures that sensitive information remains confidential, as only authorized nodes can decrypt broadcast messages. This paper proposes a group rekeying scheme based on symmetric polynomial key pre-distribution. By leveraging multivariable symmetric polynomials, a secure group key is constructed. Furthermore, the scheme incorporates a dynamic rekeying mechanism to update the group key whenever a sensor node is compromised, ensuring continuous forward and backward secrecy. Performance analysis demonstrates that the proposed scheme significantly reduces both communication overhead and computational complexity compared to existing methods. Full article

Seismic reliability assessment of steel frame structures using nonlinear finite element analysis is often hindered by implicit limit state functions and high computational cost. To address these challenges, this study proposes a machine learning-based framework for global seismic reliability analysis. A nine-story steel frame model is established and validated through modal and pushover analysis. Global sensitivity analysis using the Sobol’ method is performed to identify key parameters governing the maximum inter-story drift ratio. Three machine learning models—PSO-SVR, PSO-XGBoost, and PSO-BPNN—are trained with the selected features and integrated into Monte Carlo simulation (MCS) for reliability calculation. The results show that the PSO-BPNN model achieves the highest accuracy with the maximum error of 1.0259% relative to direct MCS, outperforming the conventional MLE-based approach, which yields errors up to 11.9383% due to the non-standard distribution of the structural response. The impact of training sample size on model performance is also examined, with 1000 samples identified as a practical threshold for acceptable prediction accuracy. Existing code design methods require modifications based on the total probability approach for global reliability analysis. This study offers an efficient and precise methodology for seismic reliability design of steel frame structures, particularly when structural responses deviate from standard parametric distributions. Full article

To identify novel field control strategies against Pyrrhalta aenescens (Coleoptera: Chrysomelidae) and provide scientific support for its biocontrol in urban tree management, this study investigated the virulence of Beauveria bassiana against this pest under laboratory conditions, as well as its physiological and biochemical effects. Bioassays using the dipping method showed that B. bassiana was pathogenic to all developmental stages of P. aenescens, with the highest virulence observed against early-instar larvae (1st and 2nd instars). For these stages, corrected mortality and mycosis rate were positively correlated with conidial concentration, and the median lethal time (LT₅₀) was the shortest. In contrast, pupae and eggs exhibited the strongest resistance to fungal infection. In leaf-disk choice tests, larvae significantly preferred untreated leaves or those treated with low concentrations of B. bassiana, displaying a concentration-dependent repellent response to the fungus. Physiological measurements revealed that larval body length and weight gain were significantly inhibited following fungal exposure. Further analysis indicated that B. bassiana infection markedly reduced total hemocyte counts and triggered intense melanization and nodulation responses, particularly in younger larvae. Overall, these results suggest that B. bassiana has strong potential for the biological control of P. aenescens. Control measures targeting early-instar larvae are recommended for cost-effective management, providing a scientific basis for developing eco-friendly control technologies based on this entomopathogenic fungus. Full article

Acquisition costs account for a significant share of total construction project costs, underscoring the importance of purchasing and supply management for organizational success. Supplier selection and evaluation are particularly critical because they involve multiple criteria, qualitative and quantitative attributes, and several decision-makers. In the construction industry, these activities become even more complex due to sector-specific characteristics such as convergent material flows, temporary facilities, buyer–supplier conflicts, price-oriented decisions, and the volatility of project-based markets. This paper investigates the supplier evaluation process in a construction company and identifies the company’s requirements and decision-makers’ expectations. Based on the collected data, this research proposes a model aligned with the company’s characteristics and the decision-makers’ expectations. The model combines two methods: the Intuitionistic Fuzzy approach to aggregate decision-makers’ opinions and ELECTRE TRI to classify suppliers based on predefined criteria and thresholds. The proposed model handles different weights assigned to each decision-maker for each criterion without allowing compensation among criteria. This model also explores the role of symmetry in multicriteria decision-making by combining Intuitionistic Fuzzy Sets with the ELECTRE TRI method. Decision-makers validated the proposal and emphasized its simplicity and flexibility, which allow future adjustments to both the criteria weights and the decision-makers’ assigned weights. Full article

Background: Open defecation (OD) has remained a threat to the attainment of SDG 6 (sanitation and hygiene). This study measured the level of exposure to the “Clean Nigeria, Use the Toilet” campaign against open defecation, determined the level of public knowledge about open defecation-related harms and diseases, ascertained the public attitude towards open defecation, and established the prevailing defecation practices and the perceived barriers to toilet usage in Ebonyi state, the most prevalent OD state in Nigeria. Methods: The study employed a survey design, using a structured questionnaire for data collection. The multi-stage sampling technique was employed in selecting the respondents from two randomly selected Local Government Areas (LGAs) in the state. Analysis was conducted using 384 valid responses. Results: The results were presented in simple percentage frequency tables and interpreted through the descriptive method, while the Chi-Square test was used to analyse the formulated hypotheses, using the decision rule of p < 0.05. The findings show a high level of awareness of the campaign against open defecation, through the radio and community engagements by environmental activists/NGOs, even though regular access to such information was limited. The results also showed inadequate knowledge of the public health implications of open defecation, whereas good knowledge of environmental consequences was reported. The study found favourable attitudes toward OD practice and persistent open defecation, and major barriers to toilet usage include the high cost of toilet construction, lack of access to toilet facilities, poor sanitation and management of available toilets, and perceived risks of contracting infection from public toilets. However, the Chi-Square values showed that the SBCC campaign was significantly associated with knowledge, attitude, and practice (p < 0.05). Conclusions: The study concluded that localised, culturally relevant and socio-demographically targeted communication interventions, grassroot advocacy, community watch, and neighbourhood taskforce on open defecation, in addition to the provision of aids for the construction of modern toilets with water facilities, are required to combat open defecation in Ebonyi and related contexts in Nigeria. Full article

Parallel multi-blade centrifugal fans present a challenge in simultaneously reducing aerodynamic noise and maintaining efficiency. This study presents a multi-objective optimization using a radial basis function (RBF)-assisted Bayesian optimization framework, with three volute parameters (tongue radius, tongue clearance, and axial gap) as design variables. Computational fluid dynamics (CFD) combined with the Ffowcs Williams–Hawkings (FW-H) acoustic analogy was employed to evaluate noise and total pressure efficiency. To reduce computational cost, an RBF surrogate model was constructed from 30 Latin hypercube samples, achieving leave-one-out cross-validation (LOOCV) R² values of 0.978 and 0.995 for noise and efficiency, respectively. A Bayesian search using the log expected hypervolume improvement (logEHVI) acquisition function was performed on the RBF response surfaces, converging to a hypervolume of approximately 0.72, consistent with an NSGA-II benchmark. Based on household fan requirements, a 70/30 noise-efficiency weighting was adopted, yielding RBF-predicted values of 59.04 dB and 0.545 for the selected low-noise-preference candidate. An independent CFD recalculation yielded 59.19 dB and 0.554. The SPL at the characteristic frequency of 2550 Hz was reduced by 9.9 dB. Flow field analysis revealed that the optimized tongue clearance weakened the impingement on the volute tongue and suppressed unsteady vortex shedding. This framework provides an efficient strategy for multi-objective aerodynamic and acoustic optimization of parallel centrifugal fan systems. Full article

Surface cracks on mandrel bars affect product quality and production stability in seamless steel pipe manufacturing. Existing vision-based methods mainly rely on bounding-box detection, which is insufficient for precise crack delineation and geometric characterization. This study proposes a lightweight segmentation framework for online mandrel bar crack inspection using grayscale industrial images. Based on YOLO11n-seg, the framework incorporates single-channel input adaptation, lightweight network reconfiguration, and crack-oriented feature enhancement to improve the extraction of weak, thin, and irregular cracks while reducing computational cost. A dedicated industrial dataset and a sample-balancing strategy are introduced to alleviate severe crack–background imbalance. Based on the predicted pixel-level masks, crack area, projected length, maximum width, and average width are calculated for online evaluation. Experimental results show that the proposed method achieves a mask mAP@0.5 of 88.5%, a false negative rate of 1.72%, and real-time inference at 204 FPS with 3.01 GFLOPs. Field deployment further demonstrates the effectiveness of the proposed framework for online crack inspection and geometric parameter calculation of mandrel bars. Full article

According to the International Association for Suicide Prevention (IASP), over one in every 100 deaths (1.3%) in 2019 was the result of suicide, yet suicide is a highly sensitive issue in the media and often a taboo. The field of communication research has very early recognized the relevance of coverage of suicide. One of the first manuals on journalistic work in 1925 elaborated on newsworthiness of suicide reporting. This paper draws on experiences of journalists who covered suicide cases during multiple crises. There is evidence that an interview is the most appropriate practice to research sensitive topics; thus, expert interviews and episodically ethnographic interviews inform this study. Additional data was collected for analysis during (participatory) observations. The presented article is an outcome of 29 interviews with journalists and mental health professionals in Greece, Spain and Bulgaria. In total, 36 h of interviews and 20 observation protocols were collected during 8 field trips and 5 weeks in total in the field. Most of the data refers to the financial crisis of 2015 and 2016—a period when suicide rates significantly increased. However, selected interviewees were interviewed again after 7–8 years during the post-pandemic time, brutal wars and the substantial cost of living crisis. Journalists who usually give a voice and platform to suicide survivors speak their own perspective and evaluate the impact it had on their mental health and well-being. Full article

Perennial crop systems (e.g., orchards) require frequent spraying with plant protection products. Equipment plays a crucial role in assessing energy efficiency, productivity, economic performance, and the environmental impact of orchard production. In recent years some farmers have replaced conventional tractor-mounted air-blast sprayers (TMABS) and switched to unmanned ground vehicles (UGVs) or unmanned aerial vehicles (UAVs). However, there has been a lack of comparative studies on the energy and environmental assessment of these systems. This study aimed to evaluate the overall viability of different orchard spraying technologies in terms of energy efficiency, economic costs, and environmental impact. A life cycle assessment (LCA) of five sprayers was performed: a TMABS, a UGV, and three UAVs. The CML-IA methodology and SimaPro 9.5 software with the Ecoinvent v3 database were used to determine the environmental impact of the compared machines. Energy efficiency was calculated using fuel consumption data, human labor energy, and the energy embodied in the machinery. Economic viability was evaluated through capital depreciation, labor, energy consumption, consumable and maintenance cost per hectare calculation models. The results indicate that UAV systems, as compared to TMABS, can significantly reduce operational energy consumption, water use, and environmental impacts. The GWP of UAV systems was about 67% lower compared to the TMABS, while the UGV, due to lower performance efficiency, exhibited a 4% larger GWP (kg CO_2eq ha⁻¹). The findings of this study highlight that UAVs can produce the optimal results in comparison to other application methods. Full article

Aquatic environments that support tourism, including coasts, lakes, reservoirs, and estuaries, are experiencing accelerating eutrophication worldwide. This trend increases the frequency and intensity of algal blooms. These blooms undermine ecosystem services and weaken the socio-economic performance of destination areas. Despite these challenges, existing research remains fragmented. Aquatic sciences mainly examine nutrient enrichment and bloom dynamics. In contrast, tourism studies often treat blooms as episodic disturbances and rarely integrate exposure pathways, risk communication, or feedback to destination governance. This review synthesizes evidence across freshwater and marine systems to develop a coupled tourism–water ecosystem perspective. We link eutrophication drivers and bloom typologies to three dimensions. These are the degradation of tourism-supporting ecosystem services, compound health stressors, and communication filters. The first includes losses of water clarity and aesthetic value. The second involves multi-route exposure through contact, inhalation, and seafood ingestion. The third shapes perceived safety, trust, and behavioral adaptation. We further connect perceived health risks to observable tourist behaviors, including cancellation, destination substitution, and activity avoidance. These micro-level responses can aggregate into market-level demand contractions and consumption reallocation. They can also trigger regional economic cascades, including public management costs, employment impacts, and long-term reputational damage. Crucially, tourism is not merely a victim of blooms. It can also act as a reinforcing anthropogenic driver through wastewater burdens, infrastructure expansion, and pulse pressures. These pressures lower ecological resilience, especially under warming and hydrological stabilization. Finally, we identify governance leverage points. These include early-warning systems, threshold-based graded interventions, transparent risk communication, and integrated social–ecological modeling. These strategies can reduce uncertainty-driven losses and support adaptive destination management. Overall, this review reframes algal blooms as systemic social–ecological risks. It provides a structured basis for future empirical attribution and policy design in tourism-dependent waters under climate stress. Full article

Navigating hilly orchards is challenging for autonomous agricultural vehicles due to the rugged terrain and dense canopy cover. Standard environmental modeling techniques are widely used, yet they often overlook how elevation uncertainty propagates during Digital Elevation Model (DEM) reconstruction. This oversight can directly affect terrain risk assessments and navigation planning. From an error-propagation perspective, this review examines how uncertainties originating from RTK-GNSS, LiDAR, and computer vision propagate through DEM reconstruction, terrain-feature extraction, cost map construction, and path planning. We further analyze how DEM elevation errors and vertical inaccuracies affect slope estimation, roughness representation, traversability assessment, vehicle stability, and navigation safety. Finally, we highlight practical bottlenecks in hilly orchard scenarios and suggest several research priorities, including multimodal fusion, uncertainty-aware modeling, lifelong map updating, and learning-based traversability assessment. Full article

Multi-party private set union (MPSU) allows multiple parties to compute a union without disclosing private inputs, but most existing protocols focus on balanced settings with comparable input sizes. In large-repository update scenarios, a leader maintains a massive base set while contributors submit small update sets; directly using balanced MPSU makes the online cost scale with the leader’s repository size. We propose AegisUnion, an asymmetric MPSU protocol tailored to large-repository updates. AegisUnion separates repository-dependent computation from online update processing through an offline oblivious key-value store (OKVS) encoding phase. In the online phase, contributors perform private membership determination, cross-contributor private deduplication, conditional payload sharing, and secret-shared shuffling, without revealing raw inputs, repository-overlap relations, inter-contributor duplicates, or the source of each output element. Under the semi-honest model, AegisUnion tolerates any coalition of corrupted parties as long as at least one party remains honest, without non-collusion assumptions. Experiments show that, as the repository grows from $2^{14}$ to $2^{18}$, the online time remains stable at 663–715 ms. At repository size $2^{18}$ and contributor update bound $2^{10}$, AegisUnion achieves about $455\times$ and $454\times$ lower online time than symmetric-key-based MPSU and public-key-based MPSU baselines, respectively, and about $271\times$ and $575\times$ lower online communication. Full article

As the rapid development of the industrial Internet of Things (IIoT) progresses, some data in the IIoT start to present the following characteristics: huge volume, high dimensions, distributed storage across multiple devices, and restricted data sharing due to privacy protection concerns. Such data presents a significant challenge to existing data processing methods. To this end, this work proposes an intelligent distributed-data processing method with privacy protection for IIoT (I2DPM). In this method, a federated feature integrator is first designed to capture the global feature subset of the distributed data under privacy protection. Based on the captured feature subset, a many-objective feature selection model is constructed by including the feature number, feature cost, cross-entropy, accuracy, and recall as the five objectives, where these five objectives represent the key factors influencing the feature selection performance. Then, an feedback-assisted information clustering many-objective evolutionary algorithm (MaOEA-IFC) is developed to solve the constructed model and thus obtain the optimal feature subsets, which fully utilizes the ideas of feedforward and feedback control. Finally, MaOEA-IFC is first compared with five state-of-the-art methods on two benchmark test suites to validate its ability to obtain reliable experimental results, and then our method is tested on eight datasets. Extensive results demonstrate that MaOEA-IFC is highly competitive, and our method can obtain the feature subsets with good comprehensive performance on the premise of protecting data privacy. In summary, this work provides a method for processing the data with the above characteristics in IIoT. Full article

Vertebral compression fractures (VCFs) are one of the most common spinal disorders encountered clinically. Untimely diagnosis or inaccurate classification often leads to prolonged pain and functional impairment in patients. To enhance diagnostic accuracy and efficiency, this study addressed the high cost and limited applicability of computed tomography (CT) and magnetic resonance imaging (MRI) examinations by leveraging the universality and convenience of X-ray imaging. We proposed a multi-stage deep learning-based method for identifying vertebral compression fractures. The method first employs Discrete Wavelet Transform-YOLOv5 (DWT-YOLOv5) for preliminary vertebral region localization, followed by Polarized Self-Attention-UNet (PSA-UNet) for precise segmentation. Finally, a ResNet50 network incorporating a Convolutional Block Attention Module (CBAM) performs graded classification, categorizing vertebrae into four types: Non-fracture, Mild fracture, Moderate fracture, and Severe fracture. The experimental results demonstrate that the proposed method achieved average accuracy, precision, recall, specificity, and F1-score of 83.7%, 88.1%, 86.2%, 97.7%, and 87.2%, respectively. The proposed method fully leverages the cost-effectiveness and convenience of X-ray imaging, providing clinicians with an efficient and economical auxiliary diagnostic tool. It enables rapid and accurate identification of vertebral compression fractures in emergency and initial screening scenarios. Full article

Glioblastoma (GBM) is one of the most aggressive human cancers, with therapeutic failure driven by pronounced intratumoral heterogeneity, microenvironmental plasticity, immune suppression, blood–brain barrier (BBB)-related pharmacological constraints, and adaptive resistance mechanisms. A major limitation in GBM research is the lack of a human-relevant experimental system able to reproduce these dynamic features while generating interpretable, multimodal datasets. In this context, we propose a testable organ-on-chip (OoC)-extracellular vesicle (EV)-deep learning (DL) framework in which patient-derived GBM cells, endothelial cells, astrocytes, pericytes, stromal cells, and immune components are organized within perfused microphysiological systems. EVs are selectively and temporally harvested from defined compartments, and imaging, barrier-function, sensor, and EV-cargo data are integrated through modality-specific and multimodal DL architectures. This framework is intended not as an immediately validated clinical tool but as an experimental roadmap for linking EV-mediated communication to measurable phenotypes such as BBB disruption, invasion, immune reprogramming, and drug response. We critically discuss the technical requirements of BBB-on-chip systems, EV source attribution, immune-component integration, DL model selection, data scarcity, overfitting, batch effects, domain shift, regulatory barriers, cost, throughput, and reproducibility. By repositioning OoC-EV-DL integration as a staged translational strategy rather than a clinically established solution, this work aims to define a realistic and biologically grounded route for advancing precision oncology in GBM. Full article