Search Results (12,948)

The Chinese giant salamander (Andrias davidianus), a species of high ecological and conservation value, shows abnormal respiratory behaviors as early signs of health decline. Accurate assessment of its pulmonary respiration is crucial for improving captive breeding and post-breeding parental care—key strategies for its survival and population recovery. However, its nocturnal and cave-dwelling nature makes traditional observation extremely difficult. Manual monitoring suffers from poor visibility at night, while conventional detection methods often miss subtle respiratory movements, limiting behavioral and health research. To address these challenges, this study presents the first automated method for monitoring respiratory behaviors in this species. We propose Mamba-YOLO-SRC, a novel hybrid detection framework that combines Mamba and YOLO architectures to accurately identify four key behaviors: diving (Dive), head-raising (HeadUP), inhalation (Inhale), and exhalation (Exhale). The proposed model achieves a mean average precision (mAP@0.5) of 0.944, with per-class average precision scores of 0.975 for Dive, 0.925 for HeadUP, 0.948 for Exhale, and 0.928 for Inhale. Mamba-YOLO-SRC provides a feasible and referable technical solution for advancing research on the Chinese giant salamander in both captive and natural settings. Full article

The automotive panoramic roof exhibits a large-size and thin-wall geometry, with a length-to-thickness ratio approaching the thousand level. This geometric feature makes its forming quality highly sensitive to forming conditions. During the glass molding process, variations in temperature evolution, loading, and cooling parameters may lead to residual stress accumulation and springback deformation, thereby affecting dimensional accuracy and final forming quality. In this study, a full-process finite element model was established and combined with an L16(4^5) orthogonal design to investigate the effects of five key process parameters—heating temperature, holding time, quenching air velocity, quenching air pressure, and quenching time—on the mean residual stress and mean springback displacement in the glass molding process (GMP). The results showed that, within the given parameter ranges, heating temperature, holding time, and quenching time had relatively pronounced effects on the mean residual stress; the mean residual stress was relatively low when the heating temperature was 680 °C, the holding time was 3 s, and the quenching time was 12 s. Heating temperature, quenching air velocity, and quenching time had relatively pronounced effects on the mean springback displacement; the mean springback displacement was relatively low when the heating temperature was 677.5 °C, the quenching air velocity was 13 m/s, and the quenching time was 10 s. Based on the orthogonal analysis, regression models for the mean residual stress and mean springback displacement were further developed, and parameter combinations were screened using the NSGA-III method. Experimental validation showed that the relative error of the mean residual stress was controlled within 15%, indicating that the established model could, to some extent, capture the relationship between process parameters and forming quality indicators, thereby providing guidance for precision forming and process optimization of large-scale thin-walled automotive panoramic roofs. Full article

There is a growing trend towards vehicles powered by alternative energy sources due to the environmental pollution caused by fossil fuel vehicles. Electric vehicles (EVs) are thought to make a significant contribution to reducing environmental pollution. This study presents a performance comparison of field-oriented control (FOC) and finite control set-based model predictive current control (FCS-MPCC) methods for controlling PMSM motors, which are commonly preferred for EV applications. A multilevel ANPC inverter topology, which has a higher-quality power flow than classical two-level inverters, was preferred to power the PMSM. While the classical FOC method has a fixed switching frequency by including cascaded PI controllers and a pulse width modulation (PWM) modulator, the FCS-MPCC method determines a variable frequency-switching signal that minimizes the cost function by predicting the future current behavior of the PMSM using the mathematical model of the system. The performance comparison of FOC and FCS-MPCC methods was carried out by conducting real-time experimental studies. Both control algorithms were analyzed under variable speed and load conditions using the same motor and drive structure. Performance analysis of FOC and FCS-MPCC control algorithms was carried out in terms of speed tracking, torque, current, and harmonics. According to the results obtained, the total harmonic distortion (THD) value of the stator current was 7.03% in the FOC method, while it was 22.19% in the FCS-MPCC method. Furthermore, a comparative analysis was conducted on the dynamic performance of the two methods in different scenarios using the mean absolute error (MAE), root mean square error (RMSE), integral absolute error (IAE), integrated time absolute error (ITAE), and integral squared error (ISE) criteria. The FCS-MPCC method was observed to be superior in different speed scenarios according to these criteria. In terms of processor load, it was calculated as 17.09% in the FOC method and 63.75% in the FCS-MPCC method. This study is important for determining the control strategy of PMSMs used in EV drives. Full article

IoT devices operate as integrated systems spanning hardware, firmware/software layers, and communication layers. In operational settings, many faults and performance degradations are emergent: they arise from cross-layer interactions, workload changes, and telemetry artifacts, rather than a single physics-of-failure mechanism. These realities make traditional supervised fault classification difficult because labeled fault data are rarely available during deployment, and the fault surface is unknown and a priori. This paper presents a practitioner-oriented, label-free fault detection and diagnosis (FDD) pattern based on Dynamic Time Warping (DTW) for rapid implementation in production IoT telemetry. The method represents a device as a sequence of overlapping episodes and organizes telemetry into interpretable layers (hardware sensors, communication health proxies, and software/firmware-derived KPIs). A reference library of regular episodes is built from an assumed-healthy training window; new episodes are scored using constrained DTW distances against this library, while retaining per-layer and per-channel contributions for attribution. We show that production performance depends strongly on operational parameterization, including episode length, DTW constraints, robust threshold learning, and temporal validation. Within a verified-healthy evaluation window, the tuned configuration achieves an AUROC of 0.97 for the temporally structured faults DTW is suited to (bias, drift, and interaction faults, with spikes detected at an AUROC of 0.93), detecting 100% of injected faults, with a mean delay under 25 min. We further show that constant-value (stuck-at) and missing-data (dropout) faults fall outside DTW’s shape-matching scope (AUROC about 0.66) and are better served by complementary variance- and missingness-based detectors, a consequence of DTW’s shape-matching scope rather than a parameter choice. This work contributes a system-level methodological framework for deploying DTW as an IoT fault-detection-and-diagnosis capability: an episode-and-layer architecture aligned with hardware, communication, and software/firmware ownership; a label-free reference library requiring only assumed-healthy data; per-layer and per-channel attribution for cross-domain triage; and a reproducible operational tuning procedure. Together, these deliver a fast-to-deploy, scalable, and accurate first-line detector for label-scarce IoT systems. Full article

Supplier selection in healthcare is a complex multi-criteria decision-making (MCDM) challenge requiring a balance of sustainability, resilience, and operational efficiency. Traditional methods struggle with scalability and subjectivity when applied to large administrative datasets. This study introduces a transparent hybrid Machine Learning–MCDM (ML–MCDM) framework, validated using a U.S. Medicare dataset of 661 suppliers. The framework integrates eXtreme Gradient Boosting (XGBoost) and SHapley Additive exPlanations (SHAP) for criterion prioritization, the Full Consistency Method (FUCOM) for mathematically consistent weighting, and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) for final ranking. As the dataset lacks direct sustainability metrics, seven indicators were synthetically generated; thus, the results serve as proof-of-concept demonstration of the framework’s architecture. Specifically, XGBoost–SHAP is trained to predict a synthetically constructed Overall Performance Score (OPS), meaning that the resulting feature importance output constitutes an algorithmic consistency check—confirming that the pipeline correctly recovers importance signals deliberately embedded in the training target. For interpretability, suppliers were segmented into five performance profiles via K-Means: Strategic Partners (17.7%), Green Leaders (18.6%), Reliable Emergency Suppliers (18.2%), Balanced Performers (20.4%), and Developing Suppliers (25.1%). Carbon Footprint Score (0.408) and Emergency Response Capability (0.316) achieved the highest feature importance. FUCOM-derived weights prioritized On-Time Delivery Rate (0.272), Carbon Footprint Score (0.222), and Emergency Response Capability (0.220). The top supplier attained a TOPSIS closeness coefficient of 0.800, showing strong discrimination. Sensitivity analysis across four scenarios confirmed ranking robustness, maintaining Spearman correlations ρ ≥ 0.977. This ML–FUCOM–TOPSIS approach provides an auditable, scalable, and policy-relevant decision-support tool, enabling procurement managers to navigate high-dimensional data while ensuring operational continuity and environmental responsibility in healthcare supply chains. Full article

Background/Objectives: Informed consent (IC) is an essential component of medical practice; however, patients’ understanding of medical information remains challenging. This study aimed to assess both objective and self-perceived comprehension of information presented in an IC scenario and to identify factors associated with understanding. Methods: A cross-sectional study was conducted using an anonymous online questionnaire with 275 adult participants in Romania. The questionnaire included a standardized IC scenario followed by comprehension assessment questions. Each correct answer was assigned one point, generating a total comprehension score ranging from 0 to 8. Self-perceived comprehension was evaluated using a Likert scale. Statistical analyses included descriptive statistics to summarize participant characteristics and questionnaire responses, Spearman’s correlations to examine associations between self-perceived comprehension and objective comprehension scores, independent samples t-tests and ANOVA to compare comprehension scores across participant groups, and multiple linear regression to identify independent predictors of comprehension. Results: The mean comprehension score was 6.81 ± 1.48, indicating a generally high level of understanding. A moderate positive correlation was observed between objective and self-perceived comprehension (ρ = 0.35, p < 0.001). Non-healthcare participants achieved slightly higher scores than healthcare field participants (p = 0.046), while educational level was not significantly associated with comprehension score (p = 0.566). Multiple linear regression analysis identified self-perceived comprehension as a significant independent predictor of the comprehension score (β = 0.381, p < 0.001). Conclusions: Although the overall level of comprehension was high, discrepancies between self-perceived comprehension and objective comprehension were identified. These findings highlight the importance of patient-centered communication strategies and the need to actively verify patient understanding during the informed consent process to support truly informed decision-making. Full article

Human saliva is a heterogeneous bodily fluid with a complex composition, which contains antibodies, proteins, and viruses, making it applicable in clinical diagnosis. There are several advantages of the analysis of saliva samples over other biofluids, including a non-invasive and simple collection procedure for extraoral detection. Biomarker or pathogen detection in saliva can be performed with various methods: mass spectrometry, PCR, ELISA, electrochemical, and optical methods such as fluorescence, SPR, and SERS. The early detection of cancer and other disease biomarkers, as well as infectious agents, can be crucial for effective treatment and minimization of mortality from those diseases. The following paper reviews extraoral detection techniques to identify the most sensitive methods for diagnosing early and asymptomatic patients. The LODs collected and tabulated from 149 analytical papers, alongside the sensitivity, specificity, and sometimes the area under the curve (AUC) tabulated from 118 clinical studies, have all become parameters for the comparative quantitative analysis. Based on the limited but substantial number of analytical studies on the detection of cortisol in saliva (29), the electrochemical platforms demonstrated the highest sensitivity, with a geometric mean LOD of 11 pM. Within these methods, voltametric ones showed the best performance with 6 pM geometric mean LOD. Electrochemical techniques are then followed by immunoassay- and mass spectrometry-based platforms, with corresponding geometric average LOD values of 39.1 and 171 pM, respectively. However, clinical outcomes are at least as meaningful as LOD values. In terms of clinical analysis, ELISA and direct-SERS outperformed other methods, achieving balanced accuracy of approximately 87% and AUC values of 0.96 for direct SERS and 0.86 for ELISA. MS and PCR followed closely, with balanced accuracies around 84%. While the direct SERS is not yet widespread in clinical applications, its potential can be forged if the standardization issue is addressed. Full article

This contribution examines the role of artificial intelligence technologies in the co-construction of social reality, with specific attention to AI-generated data as emergent agents of knowledge production. Building on perspectives from science and technology studies and recent debates on algomorphic sociology, the contribution conceptualizes generative AI systems not as research instruments, but as active participants in epistemic processes. The analysis argues that AI-generated data exhibit a performative character: they do not simply represent social phenomena but actively contribute to their stabilization, classification, and circulation. This performativity fosters a shift from researcher-centered interpretation toward hybrid configurations in which meaning emerges through human–machine assemblages. Through a theoretical synthesis of recent methodological and epistemological reflections, the contribution highlights a transition from anthropocentric models of knowledge production to post-anthropocentric, relational frameworks in which agency, cognition, and sense-making are distributed across sociotechnical networks. The contribution concludes by outlining the implications of this shift for the future of digital social research and also for reflexivity, methodological design, and the ethics of social research, advocating a critical and adaptive stance toward AI as a co-producer of knowledge rather than a subordinate analytical tool. Full article

Accurate three-dimensional characterization of turbulent flows in natural waterways is essential for the effective design of tidal farms and other critical infrastructure situated along or across rivers. High-fidelity predictions based on the large-eddy simulation (LES) method capture the necessary physics but incur computational costs that hinder rapid scenario testing. Statistically, a relatively long history of instantaneous flow fields is required to generate reliable turbulence statistics, e.g., mean velocity and Reynolds stresses, of river flow. Such a requirement often incurs high simulation runtime and data storage costs. This study seeks to develop a neural network surrogate model that learns from a limited number of instantaneous flow realizations and approximates the outputs of the corresponding time-averaged fields with LES-level accuracy. Such a surrogate would eliminate the need to accumulate extensive ensembles, enabling faster hydrodynamic assessment and making LES-informed analyses more accessible for practical engineering decisions. Full article

Integration of LiDAR and thermal sensing has become increasingly important in robotics, infrastructure diagnostics, environmental monitoring, and autonomous perception systems. LiDAR sensors provide accurate three-dimensional geometric information but do not directly capture thermal properties of observed objects, whereas thermal cameras provide temperature distributions without explicit spatial structure. Fusion of both sensing modalities enables thermally augmented 3D scene reconstruction and spatial localization of temperature anomalies. This paper presents a practical LiDAR–thermal fusion framework for three-dimensional localization of heat sources using an Ouster OS1 LiDAR sensor and a FLIR A70 thermal camera. The proposed framework includes intrinsic thermal-camera calibration, extrinsic LiDAR–thermal calibration, multimodal data synchronization, projection of LiDAR points onto the thermal image plane, and assignment of temperature values to spatial points. Additionally, a dedicated thermally distinguishable calibration target is proposed to enable reliable multimodal feature extraction under low-contrast LWIR imaging conditions. The developed framework was experimentally validated using real radiometric thermal data and LiDAR point clouds acquired under laboratory conditions. Quantitative evaluation demonstrated reprojection errors below 1 pixel and a mean hottest-point localisation error of approximately 4.1 cm at a distance of 12.3 m. The results confirm that accurate spatial localisation of thermal anomalies can be achieved using a geometry-based multimodal fusion approach without relying on computationally expensive learning-based methods. The proposed framework emphasises practical deployment, deterministic calibration, and applicability in scenarios where limited training data or constrained computational resources make learning-based approaches difficult to apply. The proposed system may be applied to building energy diagnostics, industrial inspection, technical infrastructure monitoring, and robotic perception systems that require reliable spatial localisation of heat sources under real measurement conditions. Full article

Individual tree modeling (ITM) is an effective system for thinned stands, especially in teak (Tectona grandis Linn F.) plantations, allowing the estimation of individual-tree-specific variables. Heartwood diameter and volume have high added value and can be estimated in living trees. Therefore, we developed an ITM system for clonal teak stands capable of projecting technical intervention ages and quantifying heartwood production throughout the rotation in the Eastern Brazilian Amazon. The system included equations for total tree height, site index, and taper of both stem and heartwood, with volumes obtained by integrating the respective taper equations. Future diameters and heights were projected using models based on the algebraic difference approach (ADA) and the generalized algebraic difference approach (GADA). Ages of technical intervention were defined by the maximum mean annual increment in volume with bark. The Lundqvist-Korf-ADA base model was the most accurate in estimating future trees’ diameters and heights. The inclusion of the number of trees as a covariate to represent thinning had a significant and positive impact on variable projections. Optimal technical rotations ranged from 17.1 to 21.3 years, considering volume with bark. An increase in the proportion of heartwood was observed, reaching 78% of the diameter and 53% of the volume at rotation ages. The modeling system developed in the present study enables the estimation of technical rotation ages and the quantification of heartwood production throughout the rotation, which provides reliable information for silvicultural planning and decision-making in the management of clonal teak stands. Full article

Background: Standard consumer theory treats preferences as fixed primitives and demand as the solution to an individual optimisation problem; we instead model consumption styles as heritable strategies whose prevalence is shaped by selection and experimentation, and ask when status competition produces an over-consumption trap. Methods: We embed a reference-dependent payoff—private utility concave in own consumption, a positional benefit proportional to consumption relative to the social mean, a financial-fragility cost, and a loss-averse relative-deprivation term—into replicator–mutator dynamics over three strategies (frugal, balanced, conspicuous). Results: Status concern induces strategic complementarity, so that a rising consumption norm penalises moderate consumers and makes imitation self-reinforcing. For intermediate status weight, the system is bistable: an efficient balanced equilibrium and a Pareto-inferior conspicuous trap are separated by a tipping threshold, and the width of the bistable window equals the deprivation weight, producing hysteresis in the consumption norm. The trap persists even though the positional benefit nets to zero in any monomorphic state. Mutation—behavioural experimentation—shrinks the bistable window and can dissolve the lock-in. Conclusions: Reference-dependent demand is better captured by evolutionary dynamics than by static equilibrium, and positional externalities can lock a population into self-defeating over-consumption that interventions on the deprivation or fragility channel may unlock. Full article

Sports-based television formats combining competition, cooperation, and physical confrontation have long attracted large audiences. Since the 2000s reality television has increasingly adapted these elements, particularly through wrestling- and boxing-themed programs. This study examines the genre of celebrity boxing within the broader context of contemporary media culture, with the aim of interpreting its popularity through perspectives from communication and media theory. The analysis applies a qualitative approach drawing on concepts such as the media violence and Carey’s and Couldry’s ritual model of communication and includes an empirical case study of the Hungarian television program Sztárbox. The findings suggest that celebrity boxing operates as a pseudo-sporting spectacle that combines media violence with celebrity culture to maintain audience attention, while its dramaturgy—following Barthes’ and Jenkins’ interpretations—relies heavily on simplified moral oppositions and dramatized role-playing. These elements function as micro-rituals that structure viewer engagement and contribute to collective meaning-making beyond mere entertainment. The study concludes that the appeal of celebrity boxing lies not only in the display of physical confrontation but in its ritualized narrative framework, which reinforces social and cultural interpretations of conflict, identity, and spectacle within the logic of contemporary media environments. Full article

Introduction: Elasmobranchs are an important component of deep-water and slope ecosystems, playing a key role in benthic and demersal food webs. Many species inhabiting offshore banks of the northeastern Atlantic are characterized by low productivity and high sensitivity to fishing pressure, which makes fishery-independent assessments particularly relevant. The Porcupine Bank supports a diverse assemblage of deep-water sharks and skates, yet quantitative information derived from standardized trawl surveys remains essential to characterize community structure and support ecosystem-based management. This study aims to provide an updated overview of the composition, relative abundance, biomass, and occurrence of elasmobranch species on the Porcupine Bank. Methodology: Data were collected during the Porcupine bottom trawl survey carried out in September–October 2023. The survey used a stratified random sampling design by depth and comprised a total of 88 valid demersal trawl hauls. Results: A total of 23 elasmobranch species belonging to four orders (Carcharhiniformes, Squaliformes, Rajiformes, and Hexanchiformes) were recorded. The assemblage was dominated by deep-water sharks, particularly squaliforms and carcharhiniforms. Galeus melastomus was the most dominant species, showing the highest stratified mean biomass and abundance and occurring in the majority of hauls. Other abundant and recurrent species included Etmopterus spinax, Scyliorhinus canicula, and Deania calceus. Skates of the genera Dipturus and Leucoraja were less abundant but showed consistent occurrences across depth strata. Several deep-water species, such as Apristurus spp. and Rajella fyllae, were recorded only sporadically, with very low abundances and limited occurrence. Conclusions: The results highlight the predominance of small- to medium-sized deep-water sharks on the Porcupine Bank and the comparatively lower contribution of rajid skates. This study provides a robust description of elasmobranch assemblage structure based on standardized sampling and constitutes a valuable baseline for future monitoring and comparative assessments in offshore Atlantic ecosystems. 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