Search Results (10,706)

Modern cyber–physical infrastructures rely heavily on alarm and notification systems to direct human attention when abnormal conditions occur. These mechanisms support timely and safe responses by informing operators and occupants about potential hazards. At the same time, research in human factors has shown that repeated or excessive alerts can weaken vigilance, slow reactions, and reduce confidence in warning systems. This behavioral pattern is commonly described as alarm fatigue. This paper examines how that vulnerability can be exploited intentionally. We refer to this adversarial strategy as alarm poisoning: the deliberate injection of false or misleading alerts in order to increase alarm pressure, erode trust in the monitoring infrastructure, and degrade organizational responsiveness over time. To study this process, we develop a stochastic Cybersecurity Dynamics model representing the interaction among attackers, defenders, alarm infrastructure, and a population of employees. Employee behavior is modeled through evolving trust and fatigue levels, while the overall system is formulated as a continuous–time Markov chain and simulated using the Gillespie Stochastic Simulation Algorithm. A Monte–Carlo campaign is used to analyze the resulting socio–technical dynamics under alternative attacker strategies. The study evaluates time-dependent trust, fatigue, and alarm-pressure trajectories, the distribution of times to behavioral collapse, and defender timing through Trust–Resilience–Agility–Mitigation (TRAM) metrics. The revised analysis also includes replication-sufficiency diagnostics, one-at-a-time sensitivity analysis, and threshold-robustness checks for the collapse criterion. The results show that false alarms with high perceived severity drive alarm pressure upward and degrade trust faster than nuisance-dominated campaigns, even when the total fake-alarm intensity is held constant across strategies. Collapse timing remains highly variable across stochastic realizations, and a non-negligible fraction of runs do not reach the collapse threshold within the simulation horizon. Sensitivity analysis indicates that the main qualitative ranking of attacker strategies is robust across most tested perturbations, with fatigue recovery and defender escalation emerging as particularly influential mechanisms. Overall, the findings support the view that alarm poisoning is a credible socio–technical attack vector and highlight the importance of rapid mitigation, robust alarm management, and human-centered defensive design in cyber–physical security systems. Full article

Road safety barriers represent a core component of the road with relevant consequences on effective safety for users. Maintaining these components in adequate conditions, within the quality admissibility thresholds, in compliance with all economic and management constraints, is a primary need for road administrators. In this paper, the authors propose an original procedure to classify the state of efficiency of road safety barriers, at the network scale and relying on conventional administrative data, in an optimized BIM environment, to simplify evaluations and management procedures. Through purpose-built algorithms based on selected geometric and functional parameters of the different road barriers, the algorithm provides a preliminary classification of the various segments, evidencing attention class indicators, useful as preliminary alert signals and for anticipating detailed investigations that can ensure significant economic efficiencies. The method was tested on a 10 km long motorway segment in Italy, evidencing the potential advantages of such an innovative approach to support, as a final goal, a comprehensive infrastructure digital model for virtual inspections, evaluating road component “health” state and properly implementing maintenance strategies. This approach improves network-scale monitoring and maintenance-related activity prioritization phases for road safety barriers, leveraging administrative data. This methodology functions as a BIM-based asset screening tool, as it offers a digital decision support system that identifies critical segments, to optimize the allocation of physical resources and prioritize on-site inspections where they are most needed. Full article

Accurate forecasting of sediment accumulation under extreme hydrodynamic forcing is essential for coastal engineering design and harbor management. This study evaluates the performance of Dynamic Mode Decomposition (DMD), optimized DMD (optDMD), and optimized DMD with stability constraints (optDMDs) for reconstructing and forecasting sediment accumulation height fields at the Dilderesi River mouth under a 50-year return period flood scenario. Sediment height fields generated using Delft3D are represented through reduced-order modal decompositions and the truncation rank is determined based on reconstruction-error analysis. Although all formulations reproduce the training data with negligible error, their predictive behavior differs during temporal extrapolation. Standard DMD exhibits rapid error growth at longer lead times. The optDMD formulation improves short- and intermediate-horizon performance but shows gradual degradation at extended lead times. Optimized DMD with stability constraints provides the most consistent long-horizon forecasts, maintaining high Nash–Sutcliffe efficiency and low RMSE across the full 9 h prediction interval. Examination of the continuous-time eigenvalue distributions and modal dynamics indicates that spectral characteristics of the reduced-order representation govern forecast robustness. The results demonstrate that enforcing spectral stability within reduced-order frameworks substantially enhances morphodynamic forecasting reliability under extreme flood conditions. The proposed approach provides a computationally efficient and physically consistent tool for sediment dynamics prediction in coastal engineering applications. Full article

The efficient development of low-permeability reservoirs is critically constrained by severe geological heterogeneity, marginal permeability (<10 mD), and the consequent prevalence of low-productivity wells. Conventional intermittent production management, reliant on empirical fixed-cycle schedules, fails to adapt to dynamic reservoir behavior and wellbore conditions, leading to suboptimal energy efficiency and recovery. This study presents a physics-constrained, data-driven framework for adaptive intermittent production optimization, specifically designed to address the coupled geological-engineering complexities of such reservoirs. The methodology integrates three core innovations: (1) a hybrid flowing bottomhole pressure (FBHP) decline model coupling a “Three-Segment” wellbore pressure calculation with inflow performance relationship (IPR) curves, enabling dynamic characterization of pressure depletion; (2) a shut-in pressure buildup prediction framework combining a physically interpretable dual-exponential recovery mechanism—representing near-wellbore elastic expansion and far-field formation recharge—with a Random Forest Regression algorithm to capture the influence of geological and operational heterogeneity; and (3) a “Dual-Curve Intersection Method” that autonomously determines optimal pumping and shut-in durations by intersecting predicted pressure decline and recovery curves under geological constraints. Field implementation on 15 low-production wells in the Jiyuan Oilfield—a representative low-permeability asset—demonstrated robust performance: average pump efficiency improved from 14.3% to 14.49%, and average single-well electricity savings reached 15.61%. This work establishes a closed-loop intelligent control framework that bridges reservoir geology, wellbore hydraulics, and machine learning, offering a scalable solution for enhancing energy efficiency and production sustainability in low-permeability and unconventional resources. Full article

Accurate turbojet engine performance prediction is crucial for condition monitoring, health management, and safe operation. Conventional component-level models require iterative solutions of strongly nonlinear matching equations and are sensitive to un-modeled effects, limiting accuracy and computational efficiency. Purely data-driven models are efficient but lack explicit physical constraints, resulting in poor interpretability and generalization outside the training domain. To address these issues, this paper proposes a component-decoupled, physically constrained hybrid modeling framework for turbojet engine steady-state performance prediction using on-board measurements. The engine is decomposed into component-level neural sub-models, with physics-guided feature engineering and mutual-information-based feature selection applied to optimize inputs. Component predictions are coupled via aerothermodynamic constraints to reconstruct unmeasured parameters and thrust. Validation on steady-state test data from a 120 kgf class micro turbojet engine shows the model achieves 1.157% maximum relative deviation (MRD) and 0.226% average relative deviation (ARD) for thrust, with MRDs of key gas path parameters within 0.3%. Compared with purely data-driven models, it offers higher accuracy, better generalization, and physically consistent unmeasured parameter estimates, providing a practical approach for engine performance prediction and health management. Full article

Cryptorchidism is the most prevalent congenital anomaly of the male genitourinary tract, with an incidence of approximately 1 to 9 percent in full-term male infants, decreasing with age due to spontaneous descent. It encompasses testes that fail to descend into the scrotum, which may be intra-abdominal, inguinal, or ectopic, and can be associated with syndromic, genetic, or environmental factors. The descent process occurs in two phases: intra-abdominal, driven by gubernacular development and androgen-independent mechanisms, and inguinoscrotal, regulated by hormonal and mechanical factors including androgens and the gubernaculum. Clinically, cryptorchidism manifests as absent or hypoplastic scrotal testes, often with inguinal fullness. Palpation and physical examination are primary diagnostic tools, with imaging such as ultrasound or MRI reserved for specific cases. Surgical exploration remains the definitive diagnostic modality, especially for nonpalpable testes. Early referral, ideally before 12 months of age, is essential for timely orchidopexy, which aims to position the testes within the scrotum to reduce risks of torsion, trauma, subfertility, and malignancy. Hormonal therapy shows limited efficacy and is generally not recommended as a primary treatment modality. Long-term outcomes indicate that early orchidopexy improves spermatogenic potential and fertility. Men with a history of cryptorchidism exhibit elevated risks of subfertility and testicular germ cell tumors, with the risk being higher if surgical correction is delayed or if testes remain intra-abdominal. The increased malignancy risk persists even after orchidopexy, underscoring the importance of vigilant surveillance. Management strategies emphasize a multidisciplinary approach, combining surgical intervention with ongoing monitoring, to optimize functional and oncological outcomes. Early diagnosis, appropriate surgical treatment, and patient education are critical components in minimizing long-term complications associated with cryptorchidism. Full article

The water–land classification is fundamental for shoreline extraction and coastal habitat mapping, which is the basis of a comprehensive assessment and ecosystem-based coastal zone management. This study aims to separate water and land for coastal zones by taking advantage of both high-resolution satellite imagery and airborne lidar point clouds. Considering physical principles of optical remote sensing and lidar, we developed a prior knowledge-based localization classification approach that eliminates the need for collecting training sets and handling temporal differences across multiple data sources. Our approach first created the initial classification using the WorldView-2 (WV2) Normalized Difference Water Index. Then, the Connected Components Labeling algorithm was used to create a non-overlapping partition of the working area. The third step involved processing the water blocks using prior land cover knowledge. Finally, we used lidar point clouds to refine the initial water blocks and their neighboring areas. This classification approach showed promising results along Matagorda Bay, Texas, an approximately 2449 km² area that is covered by 26 WV2 images and 1568 lidar tiles. Full article

Accurate estimation of residential building energy consumption and associated CO₂ emissions is essential for refined urban carbon management. This study develops a hybrid framework that integrates physics-based simulation and machine learning to estimate residential building energy use and energy-related CO₂ emissions in Shenzhen in 2020. Representative building archetypes were first simulated and then used to train machine-learning models for large-scale applications. Building-level energy estimates were further combined with a bottom-up inventory to generate high-spatiotemporal-resolution maps of residential CO₂ emissions. The results show that: (1) the selected model achieved good accuracy and temporal robustness, with strong agreement between estimated and reference energy use at daily, monthly, and annual scales; (2) residential energy use was primarily driven by meteorological conditions, especially daily mean temperature and the duration of high-temperature conditions, and exhibited clear weekly and seasonal patterns, with higher values on weekends and in summer; (3) residential CO₂ emissions in Shenzhen reflected the combined effects of scale and intensity, with Longgang and Bao’an contributing the largest total emissions, Self-built residential buildings contributing the largest aggregate emissions, and Old residential buildings showing the highest average emissions per building; (4) emissions were highly concentrated in a small number of high-emission buildings, which were more frequently distributed along road-adjacent block perimeters. Overall, the proposed framework improves the fine-scale characterization of residential building CO₂ emissions and provides a useful basis for hotspot identification and targeted mitigation. Full article

Background and Objectives: Craniosynostosis repair is associated with a high perioperative transfusion rate, but preoperative prediction models remain limited. This exploratory study aimed to develop and internally validate clinically prespecified preoperative models for predicting intraoperative red blood cell transfusion in pediatric craniosynostosis surgery and to evaluate whether adding fused suture extent improved model performance. Materials and Methods: This retrospective single-center prediction model study included children who underwent craniosynostosis repair between 2014 and February 2026. Patients undergoing repeat procedures or concurrent surgery for other craniofacial anomalies were excluded. The outcome was any intraoperative red blood cell transfusion. Candidate predictors were prespecified as age, weight, American Society of Anesthesiologists Physical Status (ASA-PS), preoperative hemoglobin, preoperative platelet, and fused suture extent. Five paired baseline/full ridge-penalized logistic regression models were developed, with fused suture extent added only to the full models. Performance was evaluated using apparent and bootstrap optimism-corrected area under the receiver operating characteristic curve (AUC) and Brier score. Results: Twenty-one patients were included, and nine (42.9%) received intraoperative transfusion. Across all five comparisons, inclusion of fused suture extent improved optimism-corrected discrimination and reduced prediction error. Corrected AUC increased from 0.470 to 0.674, from 0.475 to 0.738, from 0.552 to 0.667, from 0.516 to 0.704, and from 0.466 to 0.694 across the five model pairs. The best-performing model included weight, preoperative hemoglobin, ASA-PS, and fused suture extent, with an optimism-corrected AUC of 0.738 and an optimism-corrected Brier score of 0.242. Conclusions: Inclusion of fused suture extent improved preoperative prediction of intraoperative transfusion and may support perioperative blood management planning in pediatric craniosynostosis surgery. However, external validation using larger independent cohorts is necessary prior to clinical implementation. Full article

This paper presents a novel distributed coordination framework for multi-agent robotic swarms tailored for smart transportation applications. The proposed approach addresses the critical pre-transportation phase where a fleet of mobile robots, eventually with different sizes, must converge to fixed positions around an object to ensure effective caging within a user-defined prescribed time. By leveraging a time-varying diffeomorphic mapping based on an affine transformation, the strategy embeds prescribed-time guarantees within a swarm-inspired framework that maps agents between virtual and real reference frames. This methodology ensures the simultaneous achievement of precise target convergence, finite-time stability regardless of initial conditions, and inherent collision avoidance by explicitly considering the physical footprint of each robotic unit. The control protocol is first derived for scalar systems and subsequently extended to multidimensional robotic fleets using additional diffeomorphism-based techniques, which allow for the management of multiple non-interacting swarms to reduce network communication overhead. Full article

Urban fires are highly destructive with high casualty rates, often causing significant casualties and property losses. The obstruction of the Community Emergency Corridor System is a critical factor exacerbating fire casualties, directly related to residents’ life safety and public security governance effectiveness. Currently, community emergency corridors face severe systemic bottlenecks in the coordinated development of triadic space (physical, social, and information spaces), and the lag of information space has become a fatal shortcoming restricting emergency response efficiency, highlighting the urgent need for a comprehensive evaluation framework. However, existing studies mostly focus on a single spatial dimension, lacking a systematic framework for the coordinated patency of triadic space. Based on this, this study adopts the triadic space perspective, takes 77 typical communities in China as research objects, and uses the Entropy Weighted TOPSIS method to construct an evaluation index system for the accessibility of the Community Emergency Corridor System and systematically measure its level. The results show that the patency of triadic space is unbalanced overall; social space outperforms physical and information spaces (with the latter being the lowest), reflecting deficiencies in emergency information release and acquisition. Regionally, accessibility in Northeast China is significantly higher than in other regions (Northeast > West > Central > East), and eastern China has the lowest scores in physical and information spaces due to high urbanization, dense buildings, and land scarcity. Corresponding countermeasures are proposed to address regional disparities. The triadic space evaluation framework and methodological path provide a replicable analytical tool for urban fire-oriented community emergency management and references for fire resilience governance in other countries or high-density communities. Full article

This study investigates a thermal management strategy for butyl/chloroprene rubber (IIR/CR) bladder compounds by incorporating hexagonal boron nitride (h-BN) as a thermally conductive filler to enhance heat transfer efficiency. Compounds containing 0, 10, 25, and 33 wt% h-BN were prepared via solution mixing to ensure uniform dispersion and subsequently vulcanized using a hot press. The materials were characterized in terms of morphology, cure behavior using a moving die rheometer (MDR), thermal conductivity, crosslink density, mechanical properties, and dynamic mechanical analysis (DMA). The incorporation of h-BN significantly enhanced thermal performance, nearly doubling the thermal conductivity at 33 wt%. MDR measurements demonstrated that this improved heat transfer capability accelerated the thermal onset of vulcanization, effectively reducing scorch time. Mechanical testing revealed a systematic increase in stiffness at application-relevant low strain levels (25–50%), attributed to hydrodynamic reinforcement, accompanied by a progressive increase in elongation at break. This enhanced extensibility is associated with the presence of lamellar h-BN platelets, which facilitate stress redistribution and promote dynamic chain mobility under deformation. DMA showed that h-BN incorporation increased the storage modulus and intensified the Payne effect, confirming the formation of a robust physical filler network. Overall, the incorporation of h-BN delivers a formulation pathway for energy-efficient tire curing bladders by significantly improving heat transfer efficiency and dimensional stability. Full article

This study proposes an innovative instructional approach to promote scientific literacy by integrating the Nature of Science and the Nature of Scientific Inquiry with experimental practice and the history of physics. The aim is to foster a deep understanding of how scientific knowledge is constructed and to promote informed trust in science. Using an explicit and reflective methodology, the intervention combines experimental activities with historical reflection. The core of the learning sequence is the experimental reconstruction of Galileo’s studies on falling bodies, based on the historical manuscript folio 116v, an original document that provides the empirical evidence for the law of falling bodies, illustrating the transition from raw experimental data to mathematical formalization. Through this activity, students engage with key epistemic aspects of scientific practice, including the management of uncertainty—distinguished into statistical/aleatory and structural/epistemic forms—the probabilistic nature of scientific knowledge, the predictive power of models and theories, and the underdetermination of scientific theories. Additional themes addressed include the role of thought experiments, the importance of communicating results for scrutiny and validation, the function of models as mediators between theory and phenomena, and the process of de-idealization. The study also challenges the persistent myth of a single, linear “scientific method,” highlighting instead the theory-laden character of scientific inquiry and the central role of the scientific community. This dimension is explored through the historical comparison between Galileo and Mersenne, which illustrates elements of the scientific ethos and the role of peer review as a mechanism for the correction and refinement of knowledge. The results obtained with pre-service teachers, with whom this instructional sequence was implemented, indicate that this contextualized approach facilitates the overcoming of a view of science as a set of absolute truths. Instead, it promotes a more mature understanding of science as a dynamic, provisional, and self-correcting human enterprise, while equipping future citizens with the critical tools necessary to navigate the challenges of the twenty-first century. Full article

As the core of digital infrastructure, the exceptionally rapid development of data centers (DCs) faces serious challenges due to their high electricity costs. Traditional approaches treat computational task scheduling separately from different physical control mechanisms, such as server group management, overlooking the synergistic potential between the two aspects. To address this problem, this paper proposes a computational–physical collaborative optimization model that realizes spatiotemporal task migration on the computational side and adaptive parameter regulation of IT equipment and cooling devices on the physical side. In response to the lack of global coordination in conventional distributed optimization, a two-layer partially observable Markov game (POMG) is constructed to unify global cooperative decision-making and local autonomous control. On this basis, the hierarchical multi-agent deep deterministic policy gradient (H-MADDPG) algorithm is designed by introducing task priority ranking and a variable-dimension action mask mechanism, which effectively handles the discrete–continuous hybrid action space and adapts to the dynamic variation in action dimensions caused by uncertain task arrivals. Comparative experiments with various benchmark schemes are conducted to verify the effectiveness and superiority of the proposed strategy in total cost, power usage effectiveness (PUE), resource utilization, and load balancing. Full article

Sarcopenia is increasingly recognized as a key extracardiac manifestation of heart failure (HF), contributing to functional impairment, reduced quality of life, and adverse clinical outcomes. Characterized by progressive loss of skeletal muscle mass, strength, and physical performance, it affects more than half of hospitalized HF patients. It is independently associated with increased mortality and reduced exercise capacity. The pathophysiology of sarcopenia in HF is multifactorial and closely linked to metabolic and nutritional disturbances. Chronic inflammation, neurohormonal activation, oxidative stress, endothelial dysfunction, and anabolic resistance contribute to muscle catabolism and impaired protein synthesis. These alterations are further exacerbated by inadequate dietary protein intake and micronutrient deficiencies, promoting progressive muscle wasting and functional decline. Sarcopenia may also represent an early and potentially modifiable stage in the continuum toward cardiac cachexia. This narrative review provides a comprehensive synthesis of current evidence on the epidemiology, pathophysiological mechanisms, and management of sarcopenia in HF, with particular emphasis on nutritional and metabolic determinants. Emerging data support a multimodal therapeutic approach integrating exercise training with targeted nutritional strategies, including adequate protein intake, essential amino acid supplementation, and correction of micronutrient deficiencies. However, evidence from large, well-designed trials remains limited. In summary, improved recognition and integrated management of sarcopenia in HF are essential. Future research should focus on the development of effective, nutrition-centered therapeutic strategies. Full article