Search Results (13,172)

Cost overrun is a major challenge in the construction industry. However, there is a notable lack of data from imperial studies that exhaustively identify and analyze risk factors contributing to overruns. This study aims to address this gap by systematically identifying and analyzing these risk factors. A hybrid methodology was employed. It combined a systematic literature review, a three-round Delphi process, and fuzzy set techniques. Insights from the literature review informed the first-round Delphi questionnaire. Subsequent rounds were refined based on earlier results. In the third round, experts’ opinions on the likelihood and impact of the cost risk factors were collected using a 5-point Likert scale. Finally, a fuzzy approach was employed to assess the severity of cost risk factors based on the combined effects of their likelihood and impact. The results revealed that the primary cost risk factors include escalation and fluctuation in material prices, inflation, material shortages, the country’s political instability, the country’s economic instability, delays in payment to the contractor, and delays in material procurement and delivery. Notably, the significant cost risk factors are largely beyond the contractor’s control and are closely tied to the broader political and economic conditions of the country. Full article

Artificial intelligence (AI) is a cutting-edge technology that can replicate knowledge, operation and, at some point, understanding at a human-like level. The AWARE project aims to develop an AI assistant application (ASA) designed to support air traffic control (ATC) operations by building a platform based on enhanced artificial situational awareness. One of the pillars of the ASA system is to develop a set of functionalities that mimic the behavior of human actions based on the development of technical tools. Regarding safety issues, conflict detection and resolution (CD&R) is the pillar to identify conflicts and avoid mid-air collisions. The goal is to build a CD&R that can be embedded into the ASA system and generate outputs that can be usable and valuable for ATC. CD&R tool is based on two subsystems: The CD component identifies potential separation minima infringements, while the CR module produces explainable resolution maneuvers with standardized syntax for seamless ATCO integration. CD uses a deterministic algorithmic approach grounded in trajectory prediction models, while CR implements a hierarchical decision-making architecture that emulates expert ATCO cognitive processes within a client-service paradigm where pilots serve as end-users. Full article

This study developed and validated a Self-competitive Fishing (SCF) primer qPCR system as a rapid, cost-effective alternative to next-generation sequencing (NGS) for detecting POLE exonuclease domain mutations (EDMs) in endometrial cancer. The system detects 11 pathogenic POLE EDMs using SuperSelective primers combined with wild-type-blocking oligonucleotides that prevent amplification of wild-type DNA, thereby enhancing mutant DNA detection. The validation process involved comparing specificity using genomic DNA from tumors with known POLE mutations identified by NGS. Sensitivity testing used POLE-mutated DNA diluted in wild-type DNA, while precision was confirmed by analyzing 86 endometrial cancer samples against NGS results. The SCF qPCR system demonstrated superior specificity compared to the original SuperSelective primer-based qPCR, achieving 1% mutation-detection sensitivity across various mutation points. Importantly, results from all endometrial cancer cases showed complete concordance with NGS analysis for the 11 pathogenic POLE-EDM points tested. This cost-effective and efficient SCF primer qPCR system provides an accessible method for routine molecular classification of endometrial cancer in clinical settings, offering a practical alternative to NGS for detecting pathogenic POLE mutations and supporting clinical decision-making. Full article

The 12 November 2025 G4 geomagnetic storm—the third most intense of solar cycle 25—was triggered by a complex shock-ICME (interplanetary coronal mass ejection) structure as a result of three ICMEs and driven shocks that arrived on 11–12 November. The main enhancement in the interplanetary magnetic field occurred in the sheath region behind the shock driven by the second ICME. The Dst index reached −217 nT (the SYM-H index reached −254 nT) and the maximum Kp index was 9-. To comprehensively analyze the causes of the storm and its complex effects on near-Earth space, we used a multi-instrumental data set, involving data from satellite missions (ACE, SDO, PROBA2), GNSS networks, ionosondes, optical instruments, high-frequency radars (SuperDARN-like), and cosmic ray monitors. The auroral oval expanded equatorward (down to ~35° N in America). We recorded a super equatorial plasma bubble that almost reached the auroral oval boundary. The equatorial anomaly crests intensified, exceeding 175 TECU, and shifted poleward (8–10°). At mid-latitudes, the F2 layer critical frequency exhibited a strong negative disturbance (−50%) during the main phase, followed by an unusually prolonged and intense positive phase (+100%). GPS Precise Point Positioning errors increased to 2–3 m at high latitudes and in regions affected by the equatorial bubble. The event also featured a Forbush decrease and ground-level enhancement (GLE 77 according to the database hosted by the University of Oulu) associated with the X5.1 solar flare. The results underscore the complex chain of processes from solar storm to geomagnetic and ionospheric responses, highlighting the risks to satellite-based navigation and communication systems. Full article

Background/Objectives: Factors modulating phenotypic variability in Rett syndrome (RTT, OMIM 312750) include X chromosome inactivation (XCI), type of MECP2 variant, and/or disease modifiers. Emerging evidence also points to multi-locus genetic variants. Understanding the phenotypic variability associated with multi-locus genetic diagnoses in individuals with RTT and MECP2-related disorders would be important not only for accurate diagnosis, risk stratification and clinical management but also to explain symptoms that might not be typically associated with RTT. Methods: We present a case series of five individuals with a diagnosis of RTT or an MECP2-related disorder with co-occurring genetic findings, including pathogenic variants, variants of unknown significance and chromosome duplications. Clinical features such as neurodevelopmental history and comorbid medical conditions were assessed alongside the genetic findings. Results: A review of 200 cases with RTT identified five cases (all females aged 7–27 years) with a co-occurring genetic finding. Each case harboured at least one additional genetic variant that included a beta thalassaemia trait, Calmodulin 3 (CALM3) missense variant, maternally inherited 22q12.3 to q13.1 duplication, 7p14.3 and Dynein Cytoplasmic 1 Heavy Chain 1 (DYNC1H1) variants of uncertain significance and a pathogenic Set Domain-containing protein 5 (SETD5) variant. A rare triple genetic finding was illustrated in a single case, combining MECP2, CALM3, and DYNC1H1 variants. Conclusions: This case series supports the premise that RTT and MECP2-related disorders exist in a more complex neurogenetic spectrum than previously defined. It also emphasises the complexity within MECP2-related disorders. They are not static, and in the context of severe treatment resistant epilepsy, MECP2 disorders can evolve over time, necessitating diagnostic reclassification. Although the co-occurrence of multiple genetic disorders in RTT and MECP2-related disorders is rare, these cases underscore the importance of considering cumulative genetic burden when evaluating individuals with atypical features or evolving neurodevelopmental phenotypes. Full article

Background/Objectives: The increasing use of radiopharmaceuticals in clinical practice has raised concerns regarding potential radiation exposure for healthcare personnel handling biological samples from treated patients. The objective of this study was to assess the radioactivity levels in clinically necessary blood samples taken from patients treated with radioactive iodine-131 (I-131) or lutetium-177 (Lu-177) in a real-world setting. Methods: Prospective, tertiary care single-center study. Blood samples, at the clinically necessary time points, from 220 consecutive targeted radionuclide therapies (TRTs) used to treat 151 distinct patients between October 2021 and January 2025 were included. The influences of the eGFR and the time interval between tracer administration and blood sampling on radioactivity concentration were investigated by linear regression models. The applied amount of radioactivity was excluded as a confounder by adjusting all cases to 1 GBq. Statistical programming language R was utilized and p < 0.05 was considered significant. Results: The mean age of the patients was 62 years and 52% were male. Mean radioactivity concentrations of 6 vs. 60 kBq/mL were measured at 52 vs. 13 h after application of 1.9 vs. 6.7 GBq I-131 vs. Lu-177, respectively. Better renal function and later blood sampling were both associated with lower radioactivity concentration in blood samples (each p < 0.001). Total radioactivity levels in all samples were well below the upper limits for the disposal of biological samples (1 MBq for I-131 and 10 MBq for Lu-177). Conclusions: There was only a low exposure risk for nuclear medicine personnel and laboratory staff. These findings emphasize that handling blood samples from patients treated with I-131 and Lu-177 in clinical routine is minimal. Full article

Intrusion detectors are often evaluated using average metrics at unconstrained thresholds, yet deployments require explicit control over false alarms. We investigate zero-day (out-of-distribution, OOD) intrusion detection under a target-FPR calibrated protocol, where a threshold is set on benign validation traffic to satisfy a target false positive rate α and transferred, unchanged, to a seen-test and OOD-test. Using CICIDS2017-derived host-session nodes aggregated in 1min and 5min windows, we compare tabular baselines, message-passing GNNs on a rule-based graph, and employ a method that builds a k-nearest-neighbor similarity graph with lightweight feature pre-smoothing. Robustness is measured using the OOD violation ratio, percentile tail risk, and feasibility under explicit false-alarm budgets. Base-graph GNNs exhibit heavy-tailed false-alarm amplification under OOD shifts: at α = 0.001, the p95 violation ratio reaches 68.50 (1m) and 67.95 (5m). In contrast, the proposed method reduces p95 to 3.41 (1m) and 1.15 (5m) and improves budget feasibility. We further verify robustness beyond a single held-out family by evaluating additional unseen-family splits (e.g., DDoS and DDoS+DoS) under the same calibrated operating point. We also quantify deployment-oriented cost via edge-list size and practical parsing/loading time. These findings suggest that similarity-based graphs with light pre-smoothing improve deployability under distribution shifts. Full article

Background: The early detection of chronic kidney disease (CKD) is critical to preventing progression and reducing associated morbidity. The original SCreening for Occult REnal Disease (SCORED) tool has been widely adopted for CKD screening. However, its length and inclusion of items with limited predictive value affect its practicality in specific settings. This study aimed to validate a modified version of the tool (SCORED-M), which has fewer items and improved predictive performance for the early detection of disease. Methods: A cross-sectional pilot project was conducted and the diagnostic performance of the revised tool (SCORED-M) was evaluated using receiver operating characteristic analysis, sensitivity, specificity, positive predictive value, and negative predictive value (NPV). Items were selected or excluded based on their statistical significance, odds ratios, and clinical relevance to CKD risk. The optimal threshold score for mass screening was determined through a comparative analysis. Results: A total of 116 eligible participants enrolled in this pilot study. SCORED-M, comprising six items, rather than nine, as in the original version, demonstrated superior screening performance. It achieved a higher area under curve (0.89 vs. 0.79), sensitivity (0.97), and NPV (0.97), indicating its improved capability to identify individuals with CKD and rule out those without the condition. The age-related scoring range was recalibrated from 2 to 4 points to a narrower span of 1–3 points, to moderate the influence of age as a standalone risk factor for CKD. Items with limited predictive contribution, such as ‘I am a woman’, ‘I have a history of heart attack or stroke’, and ‘I have circulation disease in my legs’, were removed, while clinically relevant variables like ‘I am diabetic’, ‘I have a history of congestive heart failure or heart failure’, ‘I have protein in my urine’, ‘I have uncontrolled high blood pressure’, and ‘I have a history of renal disease’ were retained. A threshold score of ≥4 was identified as optimal, balancing sensitivity and specificity while supporting resource-efficient screening and ensuring the reproducibility of results. Conclusions: This pilot study provided preliminary evidence that the SCORED-M tool offers a more concise and accurate approach to CKD/diagnosis. While the findings are promising, validation in larger and more diverse populations is necessary to confirm the generalizability of the model and refine it for broader clinical application in mass screening programmes. Full article

Integral inclusion systems play a significant role in applied analysis and modeling, providing an effective framework for studying various physical, engineering, and dynamical processes. In this work, the solvability of a multidimensional integral inclusion system is investigated by applying the common fixed point technique to a pair of ${\mathbb{M}}_{\alpha }$-admissible multivalued operators. The analysis is carried out within a novel double-controlled vector-valued metric structure, in which the distance is governed by two independent matrix-valued control operators; this setting strictly extends classical Perov-type and $\mathrm{b}$-metric frameworks and offers a more flexible tool for treating multidimensional and interdependent systems. Existence results are derived under a suitable contractive condition within a generalized metric structure. Several auxiliary theorems are established to support the main conclusions. To illustrate the applicability of the theoretical findings, the obtained results are utilized to ensure the existence of solutions for a multidimensional Urysohn-type integral inclusion system. A simple example demonstrates the validity of the theoretical framework and highlights the effectiveness of the adopted approach. Full article

Promoting sustainability in the building sector requires greater attention to household energy-saving behavior alongside technical efficiency measures. This exploratory longitudinal study investigates how an educational intervention influences residents’ environmental awareness, knowledge, energy-saving behaviors, and motivation, while examining psychological barriers to behavioral change—specifically procrastination and comfort prioritization. The study was conducted in two residential building types in Sétif, Algeria (high-end and mid-range housing), using a pre–post questionnaire design to assess changes before and after the intervention. Findings indicate measurable improvements across all examined determinants: mean awareness scores increased from 2.10 to 3.75, knowledge from 1.75 to 2.66, reported energy-saving behavior from 2.53 to 3.36, and motivation from 2.27 to 3.83 following the educational phase. Nonlinear regression analysis further suggests that procrastination and comfort prioritization explain 40.8% of the variance in reported energy-saving behavior, underscoring the importance of psychological determinants in residential energy use. Overall, the results point to the potential of educational and behavioral strategies to complement technical measures in promoting household energy saving. Given the exploratory and context-specific design, this study offers initial empirical insights and establishes a foundation for future research involving larger and more representative samples, a broader range of building types, and diverse sociocultural settings. Full article

Background: Modern electromechanical technologies can be integrated into strength training machines to regulate the magnitude, direction, and point of application of resistance during exercise, either through preprogrammed settings or adaptively in response to real-time kinematic data. However, this potential remains largely unexplored. The objective of this study was to investigate how these new-generation devices may be managed to enable precise control of the mechanical load applied to specific joint structures during strengthening exercises. Methods: A foundational framework of biomechanical equations was developed to establish the functional relationships between joint reaction forces and key variables, including kinematic parameters (joint angle, angular velocity, and angular acceleration) and resistance characteristics (magnitude, direction, and point of application). The analysis focused on analytically determined single-joint exercises, which are commonly employed in early-stage rehabilitation and athletic conditioning programs. Results: Application of the model to single-joint knee extension exercises demonstrated that the anterior cruciate ligament (ACL)-loading shear tibiofemoral force can be entirely eliminated throughout the full range of knee motion, without increasing either the tibiofemoral compressive force or the posterior cruciate ligament (PCL)-loading shear component, while preserving the desired peak and profile of the resistance torque. Conclusion: The proposed analytical framework enables a comprehensive understanding of how to regulate resistance parameters through advanced electromechanical technologies to minimize joint stress during single-joint strengthening exercises. Precise control of joint reaction forces during exercise is critical for the design of therapeutic and safety-enhanced training protocols. Full article

This paper provides new field-based evidence on loss aversion and short-run learning using high-frequency performance data from professional golf. Leveraging over 100,000 putts recorded during the 2020 Korea Professional Golfers’ Association (KPGA) Tour, I examine how professional golfers adjust their putting behavior in response to reference-dependent incentives and immediate feedback. The structure of golf creates a natural empirical setting to test behavioral predictions: scoring rules establish salient reference points (e.g., par), while putting decisions are discrete, individually executed, and financially consequential. I find that players are significantly more likely to convert par-saving putts than birdie attempts from equivalent distances, consistent with loss aversion and reference-dependent preferences. Par putts are also executed more aggressively, but players regulate pace to avoid costly three-putt errors, indicating strategic self-regulation under loss-framed incentives. In addition, I document robust evidence of within-hole learning: second putts—taken shortly after the first under near-identical conditions—exhibit substantially higher success rates. These patterns are confirmed in logistic regression models with nonlinear distance controls and player fixed effects. This performance gap persists across scoring frames and aligns with models of reinforcement learning and dynamic belief updating. The findings illustrate how behavioral biases and adaptive learning interact in high-stakes, real-world decisions and highlight the value of professional sports data for testing core theories in behavioral economics. Full article

This paper investigates qualitative properties of fractional delay differential equations formulated in terms of the Atangana–Baleanu–Caputo (ABC) fractional derivative of order $1<\varrho <2$. Three related problem settings are examined: equations with variable delay, the constant-delay case, and a multi-delay extension involving several discrete delay terms. For each formulation, sufficient conditions ensuring existence and uniqueness of solutions are established in both the supremum norm and an exponentially weighted Maksoud norm. The analysis is carried out using Banach’s fixed point theorem in conjunction with progressive contractions and suitable Lipschitz-type conditions. In addition, Ulam–Hyers (UH) and Ulam–Hyers–Rassias (UHR) stability results are derived, providing quantitative estimates on the sensitivity of solutions with respect to perturbations. To complement the theoretical findings, numerical examples are presented, one of which illustrates the behavior of approximate solutions for various fractional orders. Full article

We introduce and study a new class of nonlinear operators on metric spaces, called rational $(a,p)-$quasicontractions. Within this framework, we establish Greguš-type fixed-point theorems for closed, convex subsets of Banach spaces. The results establish the existence and uniqueness of fixed points, as well as the convergence of the Picard iteration for every initial guess. We show that rational $(a,p)-$quasicontractions strictly extend several classical contractive classes, including Hardy-Rogers, Kannan, Chatterjea, and rational contractions, and we provide explicit examples exhibiting the properness of these inclusions. As an application, we consider a nonlocal boundary value problem for a Caputo fractional differential equation of order $\alpha \in (1,2)$ with distributed delay and mixed nonlocal boundary conditions. By rewriting the problem as a Hammerstein-Volterra integral equation on a cone, and imposing natural growth and rational Lipschitz conditions on the delayed nonlinearity, we show that the associated Hammerstein operator is a rational $(a,p)-$quasicontraction. This yields the existence, uniqueness, and global attractivity of a positive solution. Two model fractional nonlinearities with delayed feedback are discussed in detail, along with a numerical scheme that illustrates the predicted geometric convergence of the discrete Picard iteration in the Caputo fractional setting. Full article

Ecological network optimization can enhance ecological connectivity, regional ecological stability, and carbon sink capacity. Current research on ecological networks employs single-perspective optimization, which overlooks the synergistic requirements between network topological characteristics and the dual carbon goals. It lacks a comprehensive, systemic optimization framework. Focusing on the Beijing–Tianjin–Hebei region, the work constructs an ecological network by integrating ecosystem services, morphological spatial pattern analysis (MSPA), and circuit theory. A framework integrating barrier removal, structural robustness, and carbon sink enhancement is proposed, incorporating ecological barrier identification, complex network theory, and carbon offset patterns for multi-objective structural and functional optimization. The optimized network is evaluated using structural metrics, robustness analysis, and carbon sequestration validation. The network comprises 41 ecological sources and 102 corridors, exhibiting a dense northwest and sparse southeast distribution. Ecological barriers totaling 565.56 km² are removed to improve connectivity in the region. An edge-addition strategy introduces 12 nodes and 49 edges, enhancing connectivity, stability, and carbon sink capacity. Restoration priorities are set with the phased objectives of removing barriers, connecting topological weak points, and optimizing low-value carbon offset areas. Shifting the focus from structural connectivity to integrated function, the work contributes a methodological framework for advancing ecological security and carbon neutrality in urban agglomerations. Full article