Search Results (4,548)

Background: Electroencephalographic (EEG) functional connectivity analysis requires multiple signal-processing, source-modelling, and statistical steps that can limit its adoption in clinician-led randomised controlled trials (RCTs). NeuroStat was developed as a prototype research tool to integrate this workflow; formal usability validation with clinician end-users has not yet been conducted. Methods: NeuroStat is an open-source Python/PyQt6 desktop application that integrates automated artefact removal (a Generalised Eigenvalue Decomposition for Artefact Identification [GEDAI] pathway and a traditional Artefact Subspace Reconstruction (ASR)/Independent Component Analysis (ICA)/ICLabel pathway), boundary element model (BEM) source localisation using the Desikan–Killiany atlas (68 cortical regions), Phase Lag Index (PLI) connectivity estimation across five canonical frequency bands, and RCT-oriented statistical analysis. Evaluation separated sensor-space and source-space claims: a sensor-level simulation (repeated across five independent random seeds) tested preprocessing robustness, a repeated source-space simulation tested recovery of a known cortical parcel-pair contrast after forward projection and inverse reconstruction, a PhysioNet benchmark tested posterior Desikan–Killiany alpha PLI in 20 healthy adults, and an illustrative application to 20 sessions from a published chiropractic RCT demonstrated real-world workflow applicability. Results: In the sensor-level simulation benchmark, the Traditional pathway achieved a mean absolute error of $0.168\pm 0.017$ PLI units and root mean squared error of $0.219\pm 0.045$ (mean ± SD across five independent random seeds) across all artefact conditions. In the source-space simulation, reconstructed alpha PLI for the known bilateral lateral-occipital parcel pair exceeded anterior control edges across 60 repeated condition runs (mean known-control difference = 0.105 PLI units, 95% CI 0.096–0.114; $t\left(59\right)=22.61$, $p<0.001$). In the PhysioNet source-space benchmark, posterior Desikan–Killiany alpha PLI was higher during eyes-closed than eyes-open rest (Cohen’s $d=0.85$, $p=0.001$; 16/20 subjects showing the expected direction) after ICLabel-enabled preprocessing. In the pilot RCT application, all 20 sessions completed processing without manual intervention, with default-mode network alpha PLI showing a pre-to-post change of +0.071 in the intervention group versus +0.015 in the active control group. Conclusions: NeuroStat integrates preprocessing, source-space construction, connectivity estimation, and statistical reporting within a parameter-logged desktop workflow for EEG functional connectivity studies. Current evidence supports initial technical feasibility, sensor-level preprocessing robustness for one pathway in controlled simulations, source-space recovery of a known parcel-level contrast, source-space sensitivity to an expected posterior alpha resting-state contrast, and error-free processing across 20 real RCT sessions in a pilot workflow demonstration. Formal usability testing, test–retest reliability analysis, participant-specific source-model validation, and clinical-population validation remain necessary before clinician-facing or trial-deployment claims can be made. Full article

To ensure the operational safety of the OCTABUOY platform used for offshore wind turbine installation in shallow waters, an eight-point symmetric mooring system was designed based on its octagonal structural configuration. The system provides high horizontal stiffness and balanced load distribution, enhancing stability under complex environmental conditions. Physical model tests were conducted under combined wind, wave, and current loading, considering multiple wave directions, environmental cases, and five draft conditions. The mooring tensions and six-degree-of-freedom motions were systematically analyzed to evaluate system performance and safety. Results show that the proposed mooring system effectively limits platform motions and maintains stable load-sharing characteristics. The minimum safety factor under the most unfavorable condition exceeds the design requirement. In addition, the system demonstrates good redundancy: after single-line failure, remaining mooring lines redistribute loads without progressive collapse. Draft and wave incident angle significantly influence peak tensions and motion responses, with smaller drafts and oblique wave directions producing relatively higher loads. The experimental results confirm the reliability and safety margin of the eight-point mooring system and provide practical guidance for the engineering application and operational assessment of the OCTABUOY platform in shallow-water wind installation projects. Full article

This work addresses the design and implementation of an automated system for the handling and transportation of parts, integrating speed sensors, an optimized PID controller, an HMI interface, and an industrial robotic system. The speed sensors, powered by 5 V DC, enable continuous measurement of the conveyor belt’s speed and direction of rotation, providing the feedback signal required for the control loop. The core element of the system is the implementation of a PID controller applied to a direct current motor responsible for driving the conveyor belt. This controller regulates the motor speed by analyzing the error between the reference speed and the measured speed, using proportional, integral, and derivative actions to improve system stability, reduce steady-state error, and minimize oscillations. The application of PID control makes it possible to achieve an appropriate dynamic response, ensuring accuracy and reliability in the transportation process. System monitoring and operation are carried out through a human–machine interface (HMI) developed in LOGO Web Editor, which communicates with the PLC (LOGO V8) to visualize and control the status of the conveyor belt, sensors, and control elements in real time. This interface facilitates interaction between the operator and the system, allowing both virtual and physical operation. In addition, RAPID programming is used to control the IRB 14000 industrial robot, enabling the reading of PLC signals and the execution of coordinated trajectories between both arms. The operating sequence includes picking up a part with the left arm, placing it on the conveyor belt, and, after detection by sensors and PLC control, subsequent manipulation by the right arm to a specific point. Finally, both arms return to their original position, ensuring synchronized and collision-free operation. Lastly, this work integrates scientific knowledge related to the modeling, analysis, and control of dynamic systems, particularly in the implementation of closed-loop PID control optimized using genetic algorithms. This control is applied directly to an embedded system through the use of an Arduino board as the processing and control platform. Likewise, technological knowledge associated with industrial automation, PLC programming, HMI development, and industrial robotics is incorporated. The convergence of these scientific and technological approaches results in a comprehensive and compelling project that demonstrates the practical application of theoretical concepts in a functional automated system representative of real industrial environments. Full article

Background and Clinical Significance: Osteonevi, originally described by Heidingsfeld in 1908 and later by Nanta in 1911, because of whom it is known as osteonevus of Nanta, is a rare condition with not yet fully established etiopathogenesis; Case Presentation: Herein, we report a case of a 33-year-old female patient who presented to our institution with a papilliform pigmented lesion located on the projection of the left mandibular angle, measuring 2 × 1.5 cm. The lesion had been present since childhood; however, it had increased in size by approximately 5 mm over the previous month and had become painful. Surgical excision was performed, which went uncomplicated. Histology of the resected specimen showed a dermally based, symmetrical melanocytic proliferation, without signs of dysplasia, and an underlying keratocyst with rupture, accompanied by a surrounding foreign-body-type granulomatous reaction around inert keratin flakes. A third component of the lesion was also noted, represented by foci of osteoid and myeloid metaplasia underneath the melanocytic proliferation, without direct relation to the ruptured keratocyst. Based on the morphological findings, the diagnosis of osteonevus of Nanta was established; Conclusions: Oseonevus of Nanta is an extremely rare, benign morphological finding. The etiopathogenesis of these rare lesions is not yet fully established, despite several proposed mechanisms. The differential diagnosis, while typically straightforward, is broad. Full article

Decarbonising the built environment has increased the importance of prefabricated construction, yet its cost and resource efficiency are still constrained by fragmented supply chain collaboration. This study examines how lifecycle supply chain collaboration affects cost control performance in prefabricated construction. Based on supply chain management theory and expert consultation, a conceptual model was developed and tested through structural equation modelling using 517 valid responses from stakeholders in China’s prefabricated construction supply chain. The results show that management factors across all four project phases (decision and design, component production, transportation, and construction and installation) significantly improve cost control performance, with design standardisation, production scheduling, transport logistics, quality assurance, and workforce proficiency as key drivers. Process coordination exerts a significant mediating effect, while environmental factors significantly moderate the relationships. In practical terms, the findings indicate that stakeholders should prioritise design standardisation at the early stage, strengthen coordination across production, transport, and installation activities, and enhance quality control and workforce training to reduce avoidable cost overruns and resource waste. Beyond their theoretical contribution to research on supply chain collaboration in prefabricated construction, these results offer concrete direction for practitioners seeking to improve cost efficiency and make better use of resources within industrialised building systems. Full article

Nowadays, the literature on Generative AI (GenAI) in Information Technology (IT) project management is fragmented, focusing mainly on isolated tools, specific process groups, or practitioners’ perspectives, without offering a comprehensive synthesis. Therefore, there is a lack of systematic reviews to guide researchers in effectively and responsibly leveraging GenAI, including emerging innovations such as AI agents. This paper aims to synthesize current knowledge on GenAI in IT project management, combining a PRISMA-compliant systematic review of the peer-reviewed literature, a complementary analysis of commercial and open-source platforms, and a forward-looking research agenda featuring our vision on agentic AI architectures for IT project management. For the systematic review based on academic sources we have used the Web of Science (WoS) database in our study. Studies were eligible if published between 2021 and 2026 in English, as journal articles or conference proceedings, across major publishers (IEEE, Springer, Elsevier, MDPI, ACM, and others), and indexed under computer science, engineering, or AI categories in WoS. For industry-driven analysis, sources included vendor documentation, official product pages, and publicly accessible repository specifications, selected for relevance through manual search. The review reveals that while academic research remains largely focused on prompt-based applications of foundation models such as GPT, commercial and open-source platforms have progressed toward embedding GenAI as an operational capability within project workflows. Therefore, we consider that agentic architecture represents a promising future direction for enabling autonomous task execution, collaborative decision-making, and human–AI orchestration and integration across the project lifecycle. Full article

Fire safety in high-rise buildings represents a critical challenge in the United Arab Emirates (UAE), where intensive urbanization, extreme climatic conditions, and multilayered regulatory frameworks impose unique competency demands on architects and Fire Safety Engineering (FSE) consultants. Despite this, no empirically validated competency framework exists that simultaneously addresses both professional groups and is tailored to the specificities of the UAE context. This study aimed to construct and empirically validate such a framework. A three-phase sequential exploratory mixed-method design was employed. In the first phase, a systematic literature review yielded a preliminary set of 69 competency indicators organized within a Knowledge, Skills and Attitudes (KSA) structure. In the second phase, a three-round Delphi technique with an expert panel of 18 specialists validated the set to 62 final indicators. In the third phase, importance–performance analysis (IPA) was conducted on a sample of 250 professionals actively engaged in fire safety projects across four UAE. IPA identified 16 priority competency gaps, most pronounced in digital transformation (BIM, CFD, AI; gap = 1.23), proactive client advisory competencies (gap = 1.21), and regulatory navigation and Civil Defence coordination (gap = 1.00). A counterintuitive finding emerged whereby architects systematically rated competencies higher than FSE consultants across all dimensions (all p < 0.05). Psychometric validation confirmed excellent instrument reliability (Cronbach’s Alpha > 0.95) and a theoretically consistent three-factor KSA structure explaining 70.06% of variance. The developed framework of 62 empirically validated indicators represents the first competency model of its kind for architects and FSE consultants in the Gulf Cooperation Council (GCC) region. Its findings provide a direct empirical basis for curriculum reform, Continuing Professional Development (CPD) programmes, and professional licencing standards in the UAE and across the GCC region. The study makes three original contributions: the first empirically validated UAE-specific competency framework for these professional groups; a methodological combination of Delphi, IPA, EFA, Mann–Whitney, and Kruskal–Wallis not previously applied in fire safety competency research; and empirical confirmation that 74% of indicators required original development or adaptation, demonstrating the limitations of generic international competency models in the UAE context. Full article

Fringe projection profilometry (FPP) is widely used for 3D reconstruction, but conventional single-view FPP systems suffer from inherent occlusions and shadow regions, leading to incomplete surface recovery. In this study, we propose CVA-Net, an end-to-end deep learning framework with cross-view attention (CVA) that directly reconstructs dense depth maps from multi-view fringe patterns. CVA-Net simultaneously processes four fringe images acquired from orthogonal projection directions and leverages a CVA module to explicitly model inter-view dependencies, enabling adaptive fusion of complementary information. A 3D U-Net backbone with attention gates, atrous spatial pyramid pooling (ASPP), and an auxiliary parameter estimation branch further enhances reconstruction accuracy and structural consistency via multitask learning. To support Sim2Real network training, we build a Blender-based digital twin of a multi-view FPP system and generate a large-scale synthetic dataset with perfect ground truth. Extensive experiments on both synthetic and real-world objects demonstrate that CVA-Net significantly outperforms state-of-the-art single-view methods. With a symmetric four-view configuration and fringe period of 8, CVA-Net achieves an MAE of 0.0359 mm, an MSE of 0.0379 mm² and an RMSE of 0.1947 mm, reducing the MAE, MSE, and RMSE by 32.8%, 54.1%, and 32.2%, respectively, compared to the best single-view competitor. Ablation studies validate the contribution of each architectural component, while real-system experiments demonstrate the feasibility of transferring a network trained purely on synthetic data to practical FPP measurements without domain adaptation. Although further improvements are required to enhance reconstruction accuracy under real imaging conditions, the proposed framework provides an effective initial step toward bridging the gap between digital-twin-based training and real-world multi-view FPP applications. CVA-Net provides a robust, occlusion-aware solution for multi-view FPP reconstruction. Full article

As tunnel engineering continues to advance toward deeper, longer, and more complex projects, the risks encountered during the construction phase have evolved into a combination of various disaster types and the accumulation of multiple contributing factors. Traditional empirical and semi-empirical risk management methods are increasingly revealing shortcomings in terms of timeliness, accuracy, and the ability to process multi-source data. In recent years, driven by advancements in computing power and sensor technology, artificial intelligence algorithms (AI algorithms) such as machine learning and deep learning have been rapidly adopted in tunnel construction risk management. This paper retrieved relevant literature from the Web of Science database covering the period from 2010 to 2025. After rigorous screening, 96 highly relevant papers were selected for bibliometric analysis. This paper systematically reviews research progress from two perspectives: algorithmic models and engineering applications. The review indicates that, in terms of algorithmic models, traditional machine learning, convolutional neural network, recurrent neural network, generative adversarial network, Transformer, and graph neural network constitute a multi-level technical framework encompassing feature representation, risk perception, and intelligent decision-making. In terms of applications, AI algorithms have been widely integrated into typical scenarios such as geological hazard identification and prediction, surrounding rock stability and deformation prediction, rock burst assessment and early warning, lining defect detection and structural safety assessment, construction-induced ground settlement prediction, and tunnel gas and fire hazard prediction, significantly enhancing risk identification and early warning capabilities. However, several challenges remain, including the scarcity of high-quality datasets, the prevalence of noisy, incomplete, and heterogeneous monitoring data, insufficient coupling between model interpretability and engineering mechanisms, limited cross-project transferability, and the lack of integrated management systems for multi-hazard lifecycle control. Based on this, this paper proposes future research directions in areas such as data infrastructure development, integration of mechanism constraints, and multi-hazard collaborative modeling, aiming to provide guidance for the further development of intelligent risk management in tunnel construction. Full article

The interlayer pores of 3D-printed concrete (3DPC) significantly weaken its macroscopic mechanical properties. In this study, the phase-field cohesive zone model (PF-CZM) is employed as a numerical tool to systematically investigate the weakening mechanisms and crack evolution behavior associated with pore characteristics, including pore size, morphology, spatial orientation, and arrangement, through single-factor numerical simulations with different pore numbers. The results demonstrate that the degradation induced by a single pore is controlled by its effective projection length in the direction perpendicular to the principal tensile stress, with horizontal flat pores being the most detrimental under the same porosity. In the multi-pore system, the connection angle between pores, rather than their spacing, is the key factor determining structural degradation, and a horizontal collinear arrangement is prone to triggering brittle fracture. Furthermore, locally aggregated small pores can form combined defects, whose strength-weakening effect surpasses that of isolated large pores, thereby triggering crack path competition and leading to asymmetrical structural failure. This study reveals the fracture mechanisms driven by complex pore configurations and provides a reference for strength prediction of 3DPC. Full article

We present the EGIDE (Edge-on Galaxies in the DESI survey) project—a catalog of 149,215 edge-on galaxy candidates created using the data of the DESI Legacy Imaging Survey DR10 images. The catalog size is ten times greater than its predecessor and covers more than half of the sky. It is constructed in an automatic way, utilizing the full power of manual annotations from the GalaxyZoo volunteers, implemented in the Zoobot neural model, which was fine-tuned to search for edge-on galaxies specifically. To ensure the credibility of the dataset, subsequent manual supervision was performed. The EGIDE catalog provides homogeneous SExtractor photometry in the $griz$ bands, total stellar mass estimates, redshifts for 98% of the sample, star formation rates, and other information. All of this is publicly available at The Edge-on Galaxy Database site. The preliminary analysis focused on differences between edge-on galaxies in the so-called blue sequence and red cloud populations. These galaxies demonstrate distinct properties: the number of redder galaxies decreases with increasing $a/b$ ratio faster than that of the bluer galaxies; galaxy thickness varies with galaxy color: red sequence galaxies are thicker than blue cloud galaxies; the flattening ratio $q=b/a$ increases significantly with total stellar mass $M_{★}$ only among redder cloud galaxies. It is an intriguing result that the same trend of q increasing at the high-mass end is detected by both the statistical models of figures of revolution and direct observations of edge-on galaxies in EGIDE independently. The full extent of this relationship’s validity can only be determined after properly accounting for the contributions of the bulge and the PSF. Full article

Point clouds acquired by low-cost laser scanning systems have a problem of high noise, which makes the point cloud appear as thick and geometric features blurred, while existing denoising algorithms either fail to maintain a balance between denoising and shape preservation or incur excessive computational cost. To address this issue, this paper proposes a shape-preserving denoising algorithm based on normal-guided cylindrical neighborhood and bilateral weighting. Specifically, the proposed method first optimizes the PCA-initialized normals of the point cloud by integrating curvature-based feature detection and bilateral weighting. Subsequently, a cylindrical neighborhood is constructed for each point along the optimized normal direction. Finally, a bilateral weighted projection mechanism that jointly incorporates spatial and normal features is employed, whereby the aggregated projection of neighboring points drives the displacement of the central point along the normal direction, thereby achieving point cloud denoising. Experiments are conducted on synthetic datasets and real scanned datasets. The results show that, for synthetic data denoising, the proposed method achieves the best or second-best performance in 25 out of 30 experiment cases across different models and different noise levels. For real scanned data, the section views and reconstructed mesh models demonstrate that the proposed method outperforms popular algorithms in removing complex noise while preserving geometric features. In addition, the proposed method demonstrates excellent computational efficiency, capable of denoising at a speed of processing one million points every 2.4 s, and achieves acceleration of processing speed by six times compared to the fastest competitive algorithms. Full article

The industrial Internet of Things (IoT) allows traditional electromechanical systems to be connected to digital monitoring and control platforms, especially when field devices use industrial protocols that must be integrated into web services without modifying their main operation. This work implements an IoT architecture based on the Open Systems Interconnection (OSI) model to interconnect two Variable Frequency Drives (VFDs) through a LOGO! Programmable Logic Controller (LOGO! PLC), a Human–Machine Interface (HMI), a ZLAN5143D gateway, Node-RED, Message Queuing Telemetry Transport (MQTT), and Adafruit IO. The communication integrates RS485/Modbus RTU at the field level and Modbus TCP/IP over Ethernet at the upper network level using the gateway as the protocol conversion element. The validation was performed through Modbus Poll, variable acquisition, MQTT publication, and web visualization. The results show local communication response, acquisition of frequency, voltage, current, and revolutions per minute (RPM), together with remote control of start, stop, frequency setpoint, and rotation direction. The architecture is presented as a modular solution for electromechanical applications with IoT projection. Full article

Introduction: The colossal European catfish (Silurus glanis) is the largest invasive freshwater fish on the Iberian Peninsula, reaching up to 2.8 metres and 130 kg in weight. Its large size makes it a highly valued target for recreational anglers, leading to repeated illegal introductions across several Iberian watersheds. Despite its appeal to anglers, this species is recognised as a high-impact invasive predator with substantial ecological consequences for European freshwater ecosystems. Recently, large catfish aggregations have been reported by anglers and environmentalists in several areas of Portugal and Spain. These impressive aggregations are frequently documented on videos and posted on social media networks (Facebook, WhatsApp groups, etc) or shared directly with our team members. Objective: Such records provide a valuable source of information for identifying the habitats and seasonal periods associated with aggregation behaviours and may therefore support more efficient management and population control actions. Methodology: We compiled information on European catfish aggregation events in Southern Iberia, namely date and location. The catfish aggregations were mapped, and their general habitat characteristics were described. Results: We recorded 10 catfish aggregation events, most of which occurred between May and June. These were generally located in transitional areas between lentic and lotic habitats, especially in narrower river sections. Possible explanations include hydromorphological constraints, seasonal environmental conditions, and species-specific behavioural responses, although these mechanisms require further investigation. Conclusions: Within the LIFE PREDATOR project, which focuses on the management of European catfish in the Tagus watershed, knowledge of aggregation locations is important to direct population control efforts aimed at reducing the abundance of this invasive fish. Moreover, the identification of common habitat characteristics may help predict other potential aggregation sites and improve the planning of future management actions. Full article

Introduction: Inland fisheries in their diverse forms are an important activity in Portugal, currently involving about 100,000 fishers. Despite their relevance, there is still limited knowledge regarding the economic multiplier effect associated with this activity, including its contribution to local and regional economies, its broader socio-economic impacts, and its role in promoting nature-based tourism. Objective: The INFISHERIES.PT project aims to characterize the socio-economic value of inland fisheries in Portugal. Methodology: The three main fishing activities in Portugal (professional, sport, and recreational fisheries) were considered to assess inland fisheries’ economic value. Data on annual expenditures of competitive sport anglers were collected through an online questionnaire distributed by the Portuguese Federation of Sport Fishing, while data on recreational fishers were obtained through face-to-face surveys. The analysis of professional fisheries was based on official catch declarations submitted to the national licensing authority (ICNF) between 2012 and 2024. Interim Results: Results for sport fisheries indicate an estimated mean annual direct expenditure of €6.7 million, with fishing equipment accounting for the largest share, followed by travel, meals, and accommodation. Social interaction was identified as the main motivation for recreational fishing, followed by contact with nature, as well as motivations related to peace, relaxation, and entertainment. Respondents most frequently reported annual expenditures between €100 and €499 on fishing equipment, travel, and food during fishing trips. Regarding professional fisheries, results highlight the increasing importance of non-native species in total catches, particularly the red swamp crayfish, in recent years. Native migratory species, such as the European eel, sea lamprey, and allis shad, despite lower catch volumes, maintain high market value and make a significant contribution to total revenue. Conclusions: The results obtained to date in this project indicate that freshwater fishing in Portugal is a relevant activity, both in its commercial and non-commercial forms, and plays an important economic role at local and regional levels. Moreover, sport and recreational angling, in particular, also serve as drivers of nature-based tourism, potentially contributing to increased environmental awareness among the population and pressuring authorities to maintain freshwater ecosystems in good ecological condition. Full article