Search Results (103)

Hyperledger Fabric (HLF) is a leading permissioned blockchain platform designed for enterprise applications. However, it faces significant security risks from Denial-of-Service (DoS) attacks targeting its core components. This study systematically investigated network-level DoS attack vectors against HLF, with a focus on threats to its ordering service, Membership Service Provider (MSP), peer nodes, consensus protocols, and architectural dependencies. In this research, we performed experiments on an HLF test bed to demonstrate how compromised components can be exploited to launch DoS attacks and degrade the performance and availability of the blockchain network. Key attack scenarios included manipulating block sizes to induce latency, discarding blocks to disrupt consensus, issuing malicious certificates via MSP, colluding peers to sabotage validation, flooding external clients to overwhelm resources, misconfiguring Raft consensus parameters, and disabling CouchDB to cripple data access. The experimental results reveal severe impacts on the availability, including increased latency, decreased throughput, and inaccessibility of the ledger. Our findings emphasize the need for proactive monitoring and robust defense mechanisms to detect and mitigate DoS threats. Finally, we discuss some future research directions, including lightweight machine learning tailored to HLF, enhanced monitoring by aggregating logs from multiple sources, and collaboration with industry stakeholders to deploy pilot studies of security-enhanced HLF in operational environments. Full article

Aiming at the problems of frequent voltage overruns in distribution networks and difficulties in centralized optimal dispatch due to the uncertainties of distributed renewable energy sources and bus loads, this paper proposes a dynamic cluster voltage control method considering power deficit and resource allocation in an active distribution network. First, the modularity index is constructed by considering the ability of the bus electrical coupling, and the voltage regulation resources are allocated by balancing power compensation capacity and physical connectivity. This method competes with cluster partitioning and selects pilot buses. Then, an active and reactive power coordinated control model based on non-dominated sorting genetic algorithm II (NSGA-II) is developed. The model aims to minimize voltage violations, distribution network losses, and power consumption costs. Finally, five representative control scenarios are simulated and compared on an enhanced IEEE 51 bus distribution network. The results show that the proposed strategy effectively mitigates node voltage violations, reduces the losses, and enhances resource efficiency. Full article

Traditional park designs no longer meet the diverse needs of young users amid rising visitor numbers and environmental challenges. Exploring the impact of mountain city parks on youth is crucial, yet localised studies on their spatial perceptions in such unique environments are lacking. Landscape design based on spatial perception evaluation offers a promising approach for renewing mountain parks to address these complex needs. Therefore, a pilot study was conducted in Chongqing’s Pipa Mountain and Eling Parks, involving questionnaire surveys and on-site spatial data collection. Using principal component analysis to select the visual and auditory indicators most related to environmental satisfaction in the overall park and various types of gathering spaces, the results showed that the first principal component of the visual environment in the entrance platform and key nodes (r = 0.41, r = 0.45), as well as the first principal component of the auditory environment in the entrance platform, path platform, and elevated points (r = 0.67, r = 0.85, r = 0.68), all showed significant positive correlations with environmental satisfaction (p < 0.01). Moreover, naturalness and aesthetics were identified as the main factors influencing environmental satisfaction. A random forest model analysed nonlinear relationships, ranking spatial factors by importance. Simultaneously, SHAP analysis highlighted the effects of key factors like elevation changes, green view index, colour diversity, and natural elements. Elevation changes were positively correlated with satisfaction at elevated points but showed a negative correlation in the overall park environment and other gathering spaces. This study explored space-perception dynamics in mountain city parks, proposing strategies to improve environmental quality in various gathering spaces and the park. These findings support creating liveable mountainous environments and guide “human-centred health,” quality enhancement, and sustainable development in renewing mountain city parks. Full article

Air pollution is a major environmental and public health challenge, especially in urban areas where fine-grained air quality data are essential to effective interventions. Traditional monitoring networks, while accurate, often lack spatial resolution and public engagement. This study presents a novel wearable wireless sensor node (WSN) that was developed within the Horizon Europe SOCIO-BEE project to support air quality monitoring through citizen science (CS). The low-cost, body-mounted WSN measures NO₂, O₃, and PM_2.5. Three pilot campaigns were conducted in Ancona (Italy), Maroussi (Greece), and Zaragoza (Spain), and involved diverse user groups—seniors, commuters, and students, respectively. PM_2.5 sensor data were validated through two approaches: direct comparison with reference stations and spatial clustering analysis using K-means. The results show strong correlation with official PM_2.5 data (R² = 0.75), with an average absolute error of 0.54 µg/m³ and a statistical confidence interval of ±3.3 µg/m³. In Maroussi and Zaragoza, where no reference stations were available, the clustering approach yielded low intra-cluster coefficients of variation (CV = 0.50 ± 0.40 in Maroussi, CV = 0.28 ± 0.30 in Zaragoza), indicating that the measurements had high internal consistency and spatial homogeneity. Beyond technical validation, user engagement and perceptions were evaluated through pre-/post-campaign surveys. Across all pilots, over 70% of participants reported satisfaction with the system’s usability and inclusiveness. The findings demonstrate that wearable low-cost sensors, when supported by a structured engagement and data validation framework, can provide reliable, actionable air quality data, empowering citizens and informing evidence-based environmental policy. Full article

This study explores the development and implementation of an intelligent incident management system leveraging artificial intelligence (AI), knowledge engineering, and mathematical modeling to optimize enterprise operations. Enterprise incident resolution can be conceptualized as a complex network of interdependent systems, where disruptions in one area propagate through interconnected decision nodes and resolution workflows. The system integrates advanced natural language processing (NLP) for incident classification, rule-based expert systems for actionable recommendations, and multi-objective optimization techniques for resource allocation. By modeling incident interactions as a dynamic network, we apply network-based AI techniques to optimize resource distribution and minimize systemic congestion. A three-month pilot study demonstrated significant improvements in efficiency, with a 33% reduction in response times and a 25.7% increase in resource utilization. Additionally, customer satisfaction improved by 18.4%, highlighting the system’s effectiveness in delivering timely and equitable solutions. These findings suggest that incident management in large-scale enterprise environments aligns with network science principles, where analyzing node centrality, connectivity, and flow dynamics enables more resilient and adaptive management strategies. This paper discusses the system’s architecture, performance, and potential for scalability, offering insights into the transformative role of AI within networked enterprise ecosystems. Full article

Purpose: Pancreatic cancer is known for its poor prognosis. The most effective treatment combines surgery with peri-operative chemotherapy. Current prognostic tools are designed to predict patient outcomes and inform treatment decisions based on collected data. Bayesian networks (BNs) can integrate objective data with subjective clinical insights, such as expert opinions, or they can be independently based on either element. This pilot study is one of the first efforts to incorporate expert opinions into a prognostic model using a Bayesian framework. Methods: A clinical hybrid BN was selected to model the long-term overall survival of pancreatic cancer patients. The SHELF expert judgment method was employed to enhance the BN’s effectiveness. This approach involved a two-phase protocol: an initial single-center pilot phase followed by a definitive international phase. Results: Experts generally agreed on the distribution shape among the 12 clinically relevant predictive variables identified for the BN. However, discrepancies were noted in the tumor size, age, and ASA score nodes. With regard to expert concordance for each node, tumor size, and ASA score exhibited absolute concordance, indicating a strong consensus among experts. Ca19.9 values and resectability status showed high concordance, reflecting a solid agreement among the experts. The remaining nodes showed acceptable concordance. Conclusions: This project introduces a novel clinical hybrid Bayesian network (BN) that incorporates expert elicitation and clinical variables present at diagnosis to model the survival of pancreatic cancer patients. This model aims to provide research-based evidence for more reliable prognosis predictions and improved decision-making, addressing the limitations of existing survival prediction models. A validation process will be essential to evaluate the model’s performance and clinical applicability. Full article

Objectives: To assess the efficacy of magnetic resonance (MR) lymphography with gadobutrol contrast for sentinel lymph node (SLN) mapping in early-stage oral squamous cell carcinoma (OSCC). Methods: This pilot study compared the identification of SLNs by MR lymphography using a gadolinium-based contrast agent (gadobutrol) to conventional [^99mTc]Tc-nanocolloid lymphoscintigraphy (including single-photon emission computed tomography/computed tomography (SPECT/CT)) in 10 early-stage OSCC patients undergoing SLN biopsy. The patients initially underwent conventional lymphoscintigraphy following the peritumoral administration of indocyanine green [^99mTc]Tc-nanocolloid (120 megabecquerel; ~0.5 mL). Subsequently, 0.5–1.0 mL gadobutrol was peritumorally injected, and MR imaging was acquired for 30 min. The following day, the identified SLNs were harvested and subjected to a histopathological assessment. The MR lymphography and [^99mTc]Tc-nanocolloid lymphoscintigraphy results were evaluated and compared with respect to those of the SLN identification. The reference standard consisted of a histopathological evaluation of the harvested SLNs, complementary neck dissection specimens, and follow-up data. Results: The MR lymphography detected 16 out of 27 SLNs identified by [^99mTc]Tc-nanocolloid lymphoscintigraphy, revealing an additional SLN that did not harbor metastasis. MR lymphography failed to identify any SLNs in one patient. Of the seven histopathologically positive SLNs detected by [^99mTc]Tc-nanocolloid lymphoscintigraphy, three were identified by MR lymphography. All patients remained disease-free after a median follow-up of 16 months. Compared to [^99mTc]Tc-nanocolloid lymphoscintigraphy, MR lymphography using gadobutrol achieved an SLN identification rate of 59%, a sensitivity of 75%, and a negative predictive value of 86%. Conclusions: MR lymphography using gadobutrol demonstrates limited reliability for SLN mapping in early-stage OSCC. Full article

Background: Lung cancer (LC) is the leading cause of cancer-related deaths. Although low-dose computed tomography (LD-CT) reduces mortality, its clinical use is limited by cost, radiation, and false positives. Therefore, there is an urgent need for non-invasive and cost-effective biomarkers. The Raf Kinase Inhibitor Protein (RKIP) plays a crucial role in cancer development and progression and may also contribute to regulating the tumor–immune system axis. This protein has recently been described in biological fluids. Therefore, we conducted a pilot case–control study to assess RKIP and phosphorylated RKIP (pRKIP) levels in the urine and blood of LC patients. Methods: A novel enzyme linked immunosorbent assay (ELISA) assay was used to measure RKIP and pRKIP levels in urine and blood samples of two cohorts of LC patients and healthy controls (HSs). Furthermore, the biomarkers levels were correlated with tumor characteristics. Results: Serum, but not urine, levels of RKIP were significantly elevated in LC patients, distinguishing them from low- and high-risk healthy subjects with 93% and 74% accuracy, respectively. The RKIP/pRKIP ratio (RpR score) showed an accuracy of 90% and 79% in distinguishing LC patients from HS and HR-HS, respectively. Additionally, the RpR score correlated better with dimension, stage, and lymph node involvement in the tumor group. Conclusions: The serum RKIP and pRKIP profile may be a promising novel biomarker for early-stage LC. Full article

Background: The diagnosis of Alzheimer’s disease (AD) relies on core cerebrospinal fluid (CSF) biomarkers, amyloid beta (Aβ) and tau. As the brain is then already damaged, researchers still strive to discover earlier biomarkers of disease onset and the progression of AD. Glycation, advanced glycation end products (AGEs) and oxidative modifications on proteins in CSF mirror the underlying biological mechanisms that contribute to early AD pathology. However, analyzing free AGEs in the body fluids of AD patients has led to controversial results. Thus, this pilot study aimed to test the feasibility of detecting, identifying and quantifying differentially glycated, AGE or oxidatively modified peptides in CSF proteins of AD patients (n = 5) compared to a control group (n = 5). Methods: To this end, we utilized a data-dependent (DDA) nano liquid chromatography (LC) linear ion trap-Orbitrap tandem mass spectrometry (MS/MS) ) approach and database search that included over 30 glycative and oxidative modifications in four search nodes to analyze endogenous modifications on individual peptides. Furthermore, we quantified candidate peptide abundance using LC Quan. Results: We identified 299 sites of early and advanced glycation and 53 sites of oxidatively modified tryptophan. From those, we identified 17 promising candidates as putative biomarkers (receiver operating curve-area under the curve (ROC-AUC) > 0.8), albeit without statistical significance. Conclusions: The potential candidates with higher discrimination power showed correlations with established diagnostic markers, thus hinting toward the potential of those peptides as biomarkers. Full article

Background: Repetitive transcranial magnetic stimulation (rTMS) has shown therapeutic effects in neurological patients by inducing neural plasticity. In this pilot study, we analyzed the modifying effects of high-frequency (HF-)rTMS applied to the dorsolateral prefrontal cortex (DLPFC) of patients with mild cognitive impairment (MCI) using an advanced approach of functional connectome analysis based on network control theory (NCT). Methods: Using local-to-global functional parcellation, average and modal controllability (AC/MC) were estimated for DLPFC nodes of prefrontal-lateral control networks (R/LH_Cont_PFCl_3/4) from a resting-state fMRI series acquired at three time points (T0 = baseline, T1 = T0 + 4 weeks, T2 = T1 + 20 weeks) in MCI patients receiving regular daily sessions of 10 Hz HF-rTMS (n = 10, 68.00 ± 8.16 y, 4 males) or sham (n = 10, 63.80 ± 9.95 y, 5 males) stimulation, between T0 and T1. Longitudinal (group) effects on AC/MC were assessed with non-parametric statistics. Spearman correlations (ρ) of AC/MC vs. neuropsychological (RBANS) score %change (at T1, T2 vs. T0) were calculated. Results: AC median was reduced in MCI-rTMS, compared to the control group, for RH_Cont_PFCl_3/4 at T1 and T2 (vs. T0). In MCI-rTMS patients, for RH_Cont_PFCl_3, AC % change at T1 (vs. T0) was negatively correlated with semantic fluency (ρ = −0.7939, p = 0.045) and MC % change at T2 (vs. T0) was positively correlated with story memory (ρ = 0.7416, p = 0.045). Conclusions: HF-rTMS stimulation of DLFC nodes significantly affects the controllability of the functional connectome in MCI patients. Emerging correlations between AC/MC controllability and cognitive performance changes, immediately (T1 vs. T0) and six months (T2 vs. T0) after treatment, suggest NCT could help explain the HF-rTMS impact on prefrontal-lateral control network, monitoring induced neural plasticity effects in MCI patients. Full article

To estimate the expected damage due to rockfalls and debris flows for buildings and people, it is essential to assess the response of brick walls to boulder impacts. There are scarce physical tests of the impact of boulders on brick walls, which are typical of residential buildings. A simple and low-cost experimental setup for investigating the damage of unreinforced brick walls that are subjected to a boulder’s impact is presented. The setup consists of a ramp that is adjusted with a light steel structure. Seven pilot tests for five single-leaf brick walls of a 1000 × 1000 mm² area, hit by a 72.925 kg granite boulder, and from five release heights (0.25 m, 0.50, 1.00 m, 2.50 m, and 3.00 m) are performed. The observed damage indicates that wall breakthrough occurs for translational kinetic energies as low as 500 J. The prevalent failure mechanism is local shear damage. Additionally, a numerical model to simulate the physical tests was developed using the FEM. Using the same properties as in the physical testing, the numerical model is found to realistically reproduce the displacement of a node at the centre of the impact, as well as the translational impact velocity and energy, for the same five boulder release heights. Full article

In colorectal carcinoma (CRC), tumor deposits (TDs) are described as macroscopic/microscopic nests/nodules in the lymph drainage area discontinuous with the primary mass, without identifiable lymph node (LN) tissue, and not confined to vascular or perineural spaces. A TD is categorized as pN1C only when no bona fide LN metastasis exists. However, there has been an ongoing debate on whether TDs should be counted as LNs. The fact that the origin of TDs is not fully understood adds further uncertainty. This pilot study aims to evaluate whether whole-block imaging by micro-computed tomography (micro-CT WBI) that enables three-dimensional reconstruction of whole-mount (WM) blocks can serve as a tool to assess the origin and path of CRC TDs. We evaluated whole-slide imaging (WSI) and micro-CT WBI of 20 WM blocks from a rectal cancer resection that contained TDs. Each TD was tracked through the contiguous blocks to define their origin and path. Of eleven TDs identified on WSI, six were detected on WBI. Strikingly, six of six TDs trackable through the blocks on WBI revealed an origin from the main tumor. This pilot study provided evidence that micro-CT WBI can serve as an effective tool to evaluate the origin and path of CRC TDs. Full article

A complete low-power, low-cost and wireless solution for bridge structural health monitoring is presented. This work includes monitoring nodes with modular hardware design and low power consumption based on a control and resource management board called CoreBoard, and a specific board for sensorization called SensorBoard is presented. The firmware is presented as a design of FreeRTOS parallelised tasks that carry out the management of the hardware resources and implement the Random Decrement Technique to minimize the amount of data to be transmitted over the NB-IoT network in a secure way. The presented solution is validated through the characterization of its energy consumption, which guarantees an autonomy higher than 10 years with a daily 8 min monitoring periodicity, and two deployments in a pilot laboratory structure and the Eduardo Torroja bridge in Posadas (Córdoba, Spain). The results are compared with two different calibrated commercial systems, obtaining an error lower than 1.72% in modal analysis frequencies. The architecture and the results obtained place the presented design as a new solution in the state of the art and, thanks to its autonomy, low cost and the graphical device management interface presented, allow its deployment and integration in the current IoT paradigm. Full article

This comprehensive article explores the massive MIMO (M-MIMO) design and its associated concepts, focusing on the seamless integration requirements for Beyond 5G (B5G) and 6G networks. Addressing critical aspects such as RF chain reduction, pilot contamination, cell-free MIMO, and security considerations, this article delves into the intricacies of M-MIMO in the evolving landscape of B5G. Moreover, the emerging MIMO concepts in this article include AI-enabled M-MIMO three-dimensional beamforming, reconfigurable intelligent surfaces, visible light communication, and THz spectrum utilization. This review highlights the challenges and open research issues, including Narrow Aperture Antenna Nodes, Plasmonic Antenna Arrays, Integrated Sensing with M-MIMO, and the application of federated learning in M-MIMO systems. By examining these cutting-edge developments, this article aims to advance knowledge in the field and inspire future research directions in the exciting realm of B5G and 6G networks. Full article

The sliding layer created during operation of the expanded graphite–steel combination has had a huge impact on the effectiveness of the friction process, and thus on the sustainable development of society. Knowledge of the factors determining the properties of the sliding layer will make it possible to reduce friction resistance in the future through the proper design and selection of sliding pairs for given applications. This paper studies the effect of the moisture content of expanded graphite on the formation of a sliding layer on a stainless steel surface. The tests were carried out in static contact for 30 s and dynamic contact for 15 and 30 min, for loads of 10, 20, and 30 N and speeds of 25 and 50 mm/s. To determine the changes in surface geometry due to material transfer, the Ra roughness value of the surface of stainless steel samples was measured. In order to realize the purpose of the work and evaluate the effect of moist rings on the resulting sliding layer, the results of the surface roughness of stainless steel samples working with dry and moist graphite rings were compiled. The obtained results show that the presence of water in the stainless steel-expanded graphite friction node affects the formation of a sliding layer. The resulting layer reduces the surface roughness of the cooperating materials and prevents their accelerated wear. After 5 min of work with the water-soaked graphite counter-sample, depending on the applied friction conditions, a reduction in the surface roughness of the stainless steel sample was achieved in the range of 11–18% compared to the initial value. After 30 min of operation, the surface roughness decreased by 3 to 25%. Pilot studies have shown that operating conditions influence the formation of a sliding layer in the stainless steel-expanded graphite tribological contact. This confirms the validity of conducting further research in this area. Full article