Search Results (3,880)

Multi-agent and distributed dynamical systems can exhibit coordinated behavior that is difficult to summarize in terms of independent parts. Integrated Information Theory (IIT) provides one influential notion of system-level irreducibility, but exact computation of causal $\mathrm{\Phi }$ remains intractable except in very small systems. In this work, we introduce ${\mathrm{\Phi }}_{\mathrm{spectral}}$, a scalable observer-relative statistic defined on pairwise mutual information networks extracted from multivariate time-series data. A normalized graph Laplacian and its Fiedler vector identify a bipartition of the mutual information graph, and ${\mathrm{\Phi }}_{\mathrm{spectral}}$ reports the normalized weight of informational coupling crossing that cut. The measure is inspired by IIT’s concern with irreducibility but is not equivalent to intrinsic causal $\mathrm{\Phi }$: it is pairwise, undirected, and functional/statistical rather than intervention-based. We evaluate it on four exploratory simulation regimes: random oscillators, a transitional Kuramoto-like synchronization regime, a perfectly synchronized regime, and a combinatorial threshold-linear network (CTLN). Across these cases, ${\mathrm{\Phi }}_{\mathrm{spectral}}$ is most useful as a measure of observer-relative integration under second-order dependencies, separating redundancy-dominated from transiently differentiated regimes. The current results should be read as a proof-of-concept rather than as a formal validation against exact IIT. We discuss relations to weak IIT, Integrated World Modeling Theory (IWMT), and the perturbational complexity index (PCI), and we outline the stationary benchmarking and small-system validation needed for stronger causal claims. Full article

In recent times, increasing attention has been devoted to the investigation of atmospheric processes through remote sensing in order to improve our understanding of climate dynamics and atmospheric physics. This requires accurate simulation of the spectra emitted by the Earth, from which atmospheric composition and thermodynamic conditions can be retrieved. The FORUM mission focuses on observations of the Earth’s outgoing radiation in the far-infrared spectral region, which has been only sparsely explored due to observational challenges, despite its significant contribution to the characterization of atmospheric processes. As part of the mission activities, dedicated simulations of the measurements expected from the FORUM instrument are required. Different models and codes can be employed for this purpose. Fast radiative transfer models, such as SIGMA-FORUM, efficiently simulate all-sky conditions, whereas detailed line-by-line models, such as KLIMA, have generally been limited to clear-sky applications. In this context, SIGMA-FORUM, an all-sky fast radiative transfer model operating in the 10–2760 cm⁻¹ spectral range and KLIMA, a FORTRAN-based line-by-line algorithm extensively validated under clear-sky conditions, are used to simulate FORUM radiances in both clear and cloudy atmospheres. This study extends the comparison between SIGMA-IASI/F2N and KLIMA to cloudy-sky scenarios by incorporating cloud optical properties into KLIMA using the same parametrization approach adopted in SIGMA-FORUM version 2.4. By combining complementary modeling approaches, this work enables KLIMA to simulate atmospheric radiances under all-sky conditions, thereby broadening its applicability. Full article

In this work, Filipendula ulmaria (L.) Maxim, a perennial herbaceous plant from the Rosaceae family, was considered a novel source of obtaining rutin for pharmaceutical purposes. Rutin was extracted from the plant parts collected in the flowering summer period and dried at 40 ± 3 °C. The process was carried out using the ethanol extraction and fractionation of extracted compounds, and it yields the 95 wt% purity crystalline product. The phase composition of the extracted rutin was verified by the XRD analysis and NMR measurements. It was found that 2.85% of rutin could be extracted from Filipendula ulmaria, which is 1.2 times higher than the results of similar studies. The biological activity of the isolated rutin was tested on rats. It was established in vivo that the extracted rutin normalizes blood glucose levels (glucose and glycosylated hemoglobin), insulin resistance (HOMA-IR index) and reduces the severity of dystrophic changes in the liver caused by high-fat and high-carbohydrate diets. The introduction of rutin corrects lipid profile indicators (triglycerides, cholesterol, cholesterol fractions in lipoproteins and atherogenic indices), cytolysis indicators of hepatocytes, and liver steatosis (ALT, AST/ALT, triglycerides). Thus, the novel source of rutin opens the possibility for a wide use of this flavonoid in the food technology and pharmaceutical industry. Full article

When is an unknown, confined environment traversable for a specific ground robot using only touch? We answer by (i) giving an environment-anchored definition of traversability, expressed through the max-min value $T^{★}(E;A)={sup}_{\pi \in {\Pi }_{S\to G}}{inf}_{s\in [0,1]}\varphi \left(\pi \left(s\right)\right)$, where the bottleneck margin $\varphi$ aggregates the clearance, curvature ($\rho \ge R_{min}$), slope/step, and friction constraints, and (ii) introducing an on-policy, tactile certificate (TC) that maintains a conservative, monotone lower bound $T_t$ using partial contact histories. The TC fuses pessimistic free-space from contacts and the body envelope, the M3 decaying contact memory as a risk prior, and local bend/FSR proxies; a certificate is issued when $T_t>0$ and the explored corridor graph connects S to G. Relative to Papers 1–2 (tactile traversal; offline software assurance), this work formalizes traversability itself and provides a tactile-only, online certificate computable during runs. In a retrospective analysis of 660 trials across Indoor/Outdoor/Dark lighting environments, (H1) the early TC margin predicts success and traversal time better than contact/dwell heuristics (higher AUC/$R^2$), (H2) the TC predictivity is lighting-invariant, and (H3) speed-gating M3 by a TC margin recovers part of the CB-V speed gap without degrading success. Artifacts include the TC implementation, explored-corridor graphs, and per-trial TC time series added to the Paper-1 log bundle; these materials are available from the corresponding author upon reasonable request. Full article

This study evaluates the feasibility of using two affordable thermal cameras (UNI-T UTi260M and UTi260T), which are not designed as automotive sensors, for observing pedestrians and warm objects during night-time driving under low-illumination conditions. The experimental setup includes mounting the camera on the vehicle body (e.g., side mirror area/roof), recording road scenes in urban and rural environments, and selecting representative frames for qualitative and quantitative analysis. The study assesses: (i) observable pedestrian detectability in unlit road sections and under oncoming headlight glare, where visible cameras often lose contrast; (ii) the influence of low ambient temperature and strong cold wind on image appearance (including “whitening”/contrast shifts); and (iii) workflow differences, where UTi260M relies on a smartphone application for streaming/recording, while UTi260T supports PC-based image analysis and temperature-profile visualization. In addition, a calibration-based geometric method is proposed for approximate pedestrian distance estimation from single frames using silhouette pixel height and a regression model based on 1$/h_{px}$, valid for a specific mounting configuration and a known subject height. Results indicate that both cameras can highlight warm objects relative to the background and support visual pedestrian identification at low illumination, including in the presence of oncoming headlights, with UTi260M showing more stable behavior in parts of the tests. This work is a feasibility study and does not claim Advanced Driver Assist Systems (ADAS) functionality; it outlines limitations, repeatability considerations, and a minimal set of metrics and procedures for future extension. All quantitative indicators derived from exported frames are explicitly treated as image-level proxy metrics, not as physical sensor characteristics. Full article

Vibrations of thin sheet-metal parts during robotic manipulation on a production line create a number of serious challenges for production process planning. Modeling the behavior of an elastic plate or shell as a function of the robot manipulator trajectory is typically performed using the finite element method (FEM) and requires significant computational effort. The time factor remains a key limitation for integrating operations involving flexible parts into the virtual commissioning process. In this work, a methodology is proposed that enables accurate real-time reproduction of the behavior of an elastic part during linear robotic manipulation. The approach is based on modeling the response of an elastic part to a prescribed base excitation using the FEM and on the development of a reduced model compliant with the FMI/FMU standard. This reduced model computes, in real time, the convolution of the precomputed base response with the acceleration profile corresponding to the robot TCP trajectory. This makes it possible to determine the total cycle duration, which consists of the part transfer time and the time required for vibration decay at the end of the trajectory down to an acceptable threshold, as well as to perform collision checking while accounting for the deformation of the flexible part. As a result, operations involving elastic parts can be integrated into the virtual commissioning process. Full article

The transformation of the labor market driven by digital technology has profoundly affected workers’ income. Based on data from the China Family Panel Studies (CFPS) 2014–2022 and the China Labor-force Dynamic Survey (CLDS) 2012–2018, this paper systematically examines the causal effects of standard employment, traditional non-standard employment (labor dispatch), and new non-standard employment (non-contract employment) on income within a unified framework. This study adopts a progressive identification strategy combining the two-way fixed-effects model, individual fixed-effects model, and event study methodology. The findings are as follows: First, new non-standard employment exhibits a significant “income penalty” effect, with its wage level being 14–15% lower than that of standard employment. This effect remains robust after controlling for individual heterogeneity. Second, dynamic analysis shows that transitioning from standard employment to new non-standard employment leads to sustained income loss, with a decline of nearly 10.8% after four years. Third, mechanism testing reveals that workers increase part-time work to compensate for income loss, but job satisfaction significantly declines, leading to a dual dilemma of “exchanging time for income” and “welfare discount.” Fourth, heterogeneity analysis shows that less educated and rural workers suffer greater shocks. The study concludes that new non-standard employment has inherent income suppression characteristics, and its effects are persistent and heterogeneous. It calls for the improvement of a labor rights protection system that adapts to new forms of employment, as well as the implementation of targeted support policies for vulnerable groups, in order to build a more equitable and secure labor market. Full article

Errors are an unavoidable part of veterinary practice; however, little is known about how veterinary support staff perceive and deal with errors in their veterinary teams. This study examined perception of errors, contributing factors, and approaches to error management among veterinary support staff in German-speaking countries. A cross-sectional online survey was conducted, including 205 fully completed questionnaires. Reported errors were most often linked to tasks such as billing, team interaction, and handling and restraining animals. The most frequently reported contributing factors were time pressure, high workload, and communication problems. Most respondents reported that errors are openly spoken up about in everyday work (68%) and discussed within the team (76%). At the same time, perceptions of feeling safe to report an error varied: while 69% reported always or mostly feeling safe to speak up, 31% felt safe only sometimes, rarely or never. In addition, 16% of participants reported having deliberately not disclosed an error. According to 81% of respondents, no structured error reporting or management system was in place in their workplace. Overall, the findings show that error handling among veterinary support staff remains largely informal and individual, highlighting an important area for further research, improved reporting structures and training. Full article

Metal additive manufacturing (AM) facilitates the production of geometrically complex components, yet its broader industrial use remains limited by the risk of defect formation and uncertainties in their detection, originating from the highly dynamic and high-temperature process environment. To make additive manufacturing more reliable and establish high-quality parts, it is important to understand how these defects form and how their characteristics appear during the process. This review explains the main causes of common defects, such as cracking, porosity, lack of fusion, and inclusions in metal AM processes, including Powder Bed Fusion and Directed Energy Deposition. It also connects main defect formation mechanisms to the optical, thermal, acoustic, and spectroscopic signals that can be measured during the process. Moreover, it is described how commonly used in situ monitoring systems work and how their signals correspond to melt pool dynamics, vapor plume, particle movement, and the solidification process for each kind of defect. An overview is provided of how data from these systems are analyzed, including the extraction of features from images, the evaluation of temperature fields, and the use of time and frequency domain techniques for various signals. By linking the physics of defect formation to measurable process signals, the interpretation of sensor data is enabled, and potential strategies for monitoring specific problems are outlined. Finally, recent developments are examined, including the integration of multiple sensors, advanced feature-representation approaches, and real-time data interpretation coupled with adaptive control. Together, these directions represent promising advances towards more intelligent and reliable monitoring systems for the future of metal AM. Full article

Gas Metal Arc Welding (GMAW) is widely adopted in automated manufacturing industries where the accurate prediction of thermal fields and welding-induced distortions is essential to ensure joint integrity of the parts; however, finite element modeling, as the most reliable non-destructive predictive approach, remains time-consuming and highly user-specialized. This work presents an automated and low computational cost thermo-mechanical finite element methodology implemented in Ansys Parametric Design Language (APDL) for the parametric analysis of GMAW T-joints, integrating automated geometry generation, meshing, heat source implementation, and thermo-mechanical modeling for different beam and weld seam dimensions under continuous or intermittent single-pass configurations. A volume element selection strategy is introduced to limit heat input calculations to the active weld pool region, achieving up to a 50% computational time reduction while maintaining high predictive accuracy, in contrast with conventional and partial selection methods. Overall script performance was validated through temperature and displacement comparisons between the numerical and experimental results of two T-joint configurations using SM490A structural steel specimens. The results demonstrate that the developed macro provides a useful tool for automated thermo-mechanical welding analysis, significantly reducing model preparation effort while enabling the evaluation of parametric T-joint geometries and welding conditions with a low computational cost focus. Full article

Machine learning (ML) is becoming an established part of alloy research, offering new ways to link composition, processing routes, and microstructure with measured properties. In this work, recent studies using ML for predicting or optimizing alloy behavior are reviewed, covering mechanical, corrosion, phase-related, and physical properties. Unlike previous reviews organized by alloy system or modeling approach, this review is structured by target property (mechanical, corrosion, phase/structure, and physical), which helps identify the input features commonly used to model each property and highlights existing gaps in data and validation. For each study, the main property of interest, dataset features, model type, algorithm choice, use of hyperparameter tuning, and validation strategy were examined. Comparing these reports shows that ensemble models such as random forest and XGBoost, together with deep neural networks, usually perform better than linear approaches. At the same time, issues related to small datasets and inconsistent reporting remain major challenges. Attention is also drawn to new directions, particularly physics-based learning and multi-objective optimization, that are changing how ML is applied in materials design. Overall, this review summarizes current practices and outlines areas where closer integration of data-driven and experimental methods could accelerate the development of next-generation alloys. Full article

The operational reliability of gears in ship propulsion systems is an important factor affecting safety, efficiency, and cost-effectiveness in ship operation. Gear failures may result in loss of propulsion, increased maintenance costs, and risks to crew safety. This paper presents an integrated methodological framework for assessing gear reliability in ship propulsion systems by integrating qualitative causal analysis, quantitative reliability growth modelling, and system dynamics simulation. The analysis is based on empirical data collected from the AMOS computerised maintenance management system for ship propulsion gear over the course of 20,000 operating hours. The Ishikawa diagram is applied as a qualitative tool to structure potential failure causes related to human, technical, material, procedural, measurement, and environmental factors. Using a system dynamics approach, a qualitative conceptual model of cause-and-effect relationships and a quantitative simulation model were developed, where the mathematical model of Goel–Okumoto reliability growth was applied to quantitatively describe the process of detecting and eliminating failures, with an exponential decrease in failure intensity over time and a high level of agreement with empirical data (R² = 0.9962), corresponding to the part of the bathtub curve related to the running-in of ship systems. The system dynamics simulation implemented in the POWERSIM environment integrates the analytically estimated model parameters and provides a dynamic representation of the relationships between failure intensity, cumulative failures, reliability, and the mean time between failures. The scientific contribution of this work lies in the structured integration of established methods into a single analytical framework, enabling coherent interpretation of empirical reliability data under real operating conditions. The results provide a methodological basis for developing predictive maintenance tools, optimising maintenance strategies, and improving the safety of ship propulsion systems. Full article

Injection molding is widely used for plastic parts, but its performance is limited by the cooling stage, which dominates cycle time and affects dimensional stability and energy consumption. Conformal cooling channels, which can be manufactured using additive technologies, improve thermal efficiency but introduce a high-dimensional design problem. This work proposes an integrated methodology for optimizing injection molds with conformal cooling channels that combines parametric CAD (Computer-Aided Drawing), simulation, non-linear principal component analysis, artificial neural network, and multi-objective evolutionary optimization. The workflow is applied to a case study with five cooling layouts. An initial set of 36 metrics related to temperature gradients, warpage, shrinkage, and energy is reduced to a small number of latent objectives, simplifying the search space while preserving the main physical trends. Artificial neural networks surrogates accurately reproduce numerical results, enabling exploration of the design space at a fraction of the computational cost. The optimization yields diverse Pareto-optimal solutions that balance cycle time, dimensional stability, and energy consumption, assisting the design of more sustainable injection molds. Sensitivity analysis identifies mold temperature and channel position/diameter as key design levers. The proposed methodology reduces dependence on expensive simulations and is readily transferable to industrial mold design. Full article

Aiming at satisfying the micro-dynamic lubrication requirements of moving parts in spacecraft on-orbit operation, this paper proposes a micro-oil supply scheme based on a piezoelectric drive. The working mode and transient sound pressure characteristics of the micro-oil supply device were analyzed by the numerical simulation method, and the influence of driving voltage and oil dynamic viscosity on droplet growth and desorption behavior was investigated. The research results show the following: (1) The driving voltage is a key external control parameter that affects the droplet ejection characteristics. The increase of its amplitude will promote the droplet morphology to change from a stable and concentrated overall state to a dispersed state with multi-satellite droplets and easy necking fracture. At the same time, the droplet size is significantly reduced, the distribution tends to be discretized and the droplet velocity and droplet mass are simultaneously improved. Under the 220 V voltage condition, the droplet ejection velocity can reach 17.8 m/s, and the single ejection mass can reach 6.98 mg, which can realize the rapid and large-scale delivery of droplets. (2) Dynamic viscosity is the core intrinsic parameter to determine the droplet ejection characteristics. When the value of dynamic viscosity increases, the droplet morphology will change from the wire-like fracture characteristics of ‘spherical head + satellite droplet developed’ to the droplet-like structure with overall concentration and stable mechanical state. The average droplet size increased and the distribution was more aggregated. The droplet velocity and droplet mass decreased significantly. When the viscosity increased to 0.16 Pa·s, the droplet ejection velocity decreased to 1.14 m/s, and the single ejection mass decreased to 0.35 mg. Full article

The emergency evacuation of visually impaired individuals during fire incidents presents critical challenges that require innovative technological solutions. While existing evacuation systems provide static route guidance, they fail to adapt dynamically to evolving fire conditions, blocked passages, or dangerous zones in buildings with multiple routes and exits. This paper presents a comprehensive implementation of a mobile application built with Flutter/Dart that addresses these limitations by enabling real-time, dynamic route computation based on live sensor data. The presented system operates in a decentralized manner, performing all critical computations on-device to ensure its functionality even when some parts of the building infrastructure fail. A dynamic route calculation modified Dijkstra’s algorithm was implemented on each user’s phone for guidance. If initial path adjustments are needed, they are computed from sensor data to evaluate fire evolution and other relevant factors, including the user’s current position and crowd congestion. An audio–visual interface was designed to provide navigation instructions and to help users follow safety routes efficiently. Field testing with visually impaired participants demonstrated significant improvements in evacuation efficiency, with shorter evacuation times than traditional static guidance approaches. The system architecture complies with international fire safety standards while maintaining user privacy through a no-tracking design philosophy. This work contributes to both theoretical advances in adaptive evacuation algorithms and practical insights for deploying assistive technologies in emergency scenarios. Full article