Search Results (5,255)

Objectives: The aim of this study was to evaluate the psychometric properties of the Pain Catastrophizing Scale (PCS) in patients with carpometacarpal osteoarthritis of the thumb. Methods: In this cross-sectional register-based study of 253 patients with carpometacarpal osteoarthritis of the thumb, a two-parameter item response theory analysis was used to evaluate the items’ difficulty and discrimination parameters. Results: Of 253 patients, 245 (57%) were women. The mean age was 56.0 (SD 16.5) years. The mean total PCS score was 14.0 (SD 10.5) points. Difficulty estimates were distributed fairly evenly across the item score scale, with a slight shift towards higher scores. Discrimination of both total and subscale scores was perfect, varying from 1.91 to 2.84. Conclusions: PCS was able to discriminate well between different levels of catastrophizing. PCS performed slightly more accurately when the catastrophizing level was above average in the studied sample. PCS can be recommended for clinical use when assessing catastrophizing in patients with carpometacarpal osteoarthritis of the thumb. Full article

Autonomous driving in dense traffic demands policies that ensure safety, accurate path tracking, and ride comfort, yet reinforcement learning (RL) alone suffers from low sample efficiency and weak safety guarantees, while classical artificial potential field (APF) methods lack adaptability to dynamic scenarios. This paper proposes PIPF-TD3, which integrates APF theory with the Twin Delayed Deep Deterministic Policy Gradient (TD3) by embedding composite potential values and Doppler-weighted gradients as physics-informed features into the state vector. A Hybrid A* planner generates a reference path encoded as an attractive field; repulsive fields model nearby obstacles using real-time perception data; and a multi-objective reward function jointly optimizes path tracking, collision avoidance, and ride comfort. Experiments in CARLA 0.9.14 across two scenarios—a highway segment with mixed obstacles and a signalized intersection with conflicting turning movements—show that PIPF-TD3 achieves 100% task completion with zero collisions, whereas TD3 without potential field guidance suffers a 90% collision rate. PIPF-TD3 reduces mean cross-track error to 0.12 m (72.1% reduction over the rule-based FSM baseline), maintains 67.0% larger safety clearance, and yields RMS longitudinal and lateral accelerations of 1.12 and 0.75 m/s², outperforming the FSM by 37.1% and 42.7%. These results confirm that Doppler-weighted physical priors substantially enhance RL-based driving safety and quality in complex traffic conditions. Full article

In this study, we investigate how corporate policies are influenced by the presence of rookie independent directors (RIDs). We hypothesize that RIDs, due to their inexperience, impact corporate policies in ways that may amplify agency problems. Specifically, firms with RIDs demonstrate higher investment in R&D and capital expenditure, increased leverage (both short- and long-term), enhanced liquidity (cash holdings and working capital), and elevated risk-taking, while their presence leads to a conservative payout policy. Using a sample of Chinese-listed firms from 2008 to 2022, our findings confirm these predictions. Additional analyses reveal that RIDs’ effects are more pronounced in high-CEO-power environments, where their limited governance capabilities may align with managerial interests, exacerbating financial risks. This study contributes to the corporate governance literature by integrating upper echelon and agency theories, shedding light on the dual-edged role of RIDs in shaping corporate outcomes. Full article

Evaluating emotional understanding in Large Language Models (LLMs) is challenging because assessments are subjective, ambiguous, multidimensional, and sensitive to controllable generation parameters. We developed a unified mathematical framework for characterizing LLM “emotional individuality” that integrates softmax sampling–temperature control (the decoding-time temperature parameter exposed by the API and typically used to modulate output randomness during token generation), fuzzy set theory with Shannon-type fuzzy entropy, and persona-based cognitive diversity analysis. We evaluated 36 API-accessible LLMs from seven major vendors on Japanese literary texts, using four personas each assigned a sampling temperature ($T\in \{0.1,0.4,0.7,0.9\}$), yielding 4227 /4320 trial responses (97.8% coverage), of which 4067/4227 contained valid numeric emotion scores (96.2%). Temperature controllability varied approximately 25-fold (${\kappa }_M\in [0.039,0.982]$) with both positive and negative temperature–variance relationships across models. Because each sampling temperature is deterministically assigned to a persona in our design, ${\kappa }_M$ should be interpreted as an operational temperature–variance association across persona conditions rather than an isolated causal temperature effect. The model-level mean fuzzy entropy ranged from approximately 0.40 to 0.66, and the numerical stability consistency scores ranged from approximately 0.548 to 0.780. We also observed text-dependent structure, including genre-specific variation in the Interest–Sadness relationship. For practitioners, the framework is most directly useful as a benchmark-design and model-screening template for structured emotion-scoring tasks; its empirical conclusions remain limited to the present Japanese literary, text-only setting. Full article

Traditional fuzzy decision-making often relies on manual expert calibration, which is labor-intensive and susceptible to subjective bias. This study addresses these limitations by proposing a novel framework that transforms the intrinsic probabilistic outputs of Large Language Models (LLMs) into Triangular Fuzzy Numbers (TFNs). We introduce a multi-temperature sampling strategy coupled with weighted quantile aggregation and an adaptive interval adjustment mechanism to systematically map model stochasticity to fuzzy possibility distributions. Empirical validation on a structured prototype dataset demonstrates that the proposed method achieves high consistency with expert consensus, with GPT-4.2 exhibiting superior central accuracy and Gemini-2.5 excelling in uncertainty coverage. Furthermore, in complex unstructured scenarios involving business public opinion, the integration of Model Context Protocol (MCP) and Retrieval-Augmented Generation (RAG) significantly corrects cognitive biases and converges uncertainty boundaries. This research establishes a rigorous pathway from generative AI probabilities to fuzzy decision theory, offering a robust automated solution for quantitative risk assessment and intelligent decision support. Full article

Amid the new wave of technological revolution and industrial transformation, new quality productive forces (NQPFs) have become the key to a firm’s sustainable development. To help enterprises accelerate the improvement of their NQPFs, grounded in signaling theory, this paper takes data of China’s A-share listed companies from 2015 to 2024 as the research sample and uses a two-way fixed effects model to empirically examine whether and how superior ESG performance, serving as a high-quality signal, fosters NQPFs. The results show the following. There is a significant positive relationship between corporate ESG performance and NQPFs. This finding remains robust across a series of checks, including replacing the key explanatory variable, removing outlier years and cities, and addressing endogenous problems through instrumental-variable estimation. Heterogeneity tests reveal that the effect is more pronounced among non-state-owned firms and those located in Northeast China, whereas it is statistically insignificant for firms in Western China. Mechanism analysis indicates that ESG performance boosts NQPFs indirectly by raising analyst attention and investor confidence. Overall, this paper not only enriches research perspectives on the relationship between corporate ESG performance and NQPFs, but also offers theoretical support and practical reference for the formulation of corporate ESG strategies and the precise policy-making of governments. Full article

A two-dimensional segmentation model based on the P-matrix array was developed to simulate surface acoustic wave (SAW) delay-line devices under irregular loading. Building on coupling-of-modes (COM) theory and P-matrix model, a channelization approach was introduced to enhance conventional response simulation, enabling the systematic extraction of frequency and phase characteristics under varying spatial load distributions. Experimental verification was conducted using SAW devices fabricated by depositing aluminum interdigital transducers (IDTs) on Y-cut 35° quartz crystals through semiconductor lithography. The results demonstrate that the two-dimensional segmentation method effectively and accurately simulates the response of SAW delay line devices under various non-uniform and irregular mass loading distributions, both the phase shift and frequency shift exhibit linear proportionality to the loaded area (R² > 0.99), while the amplitude-frequency characteristics remain stable with increasing load coverage, showing no observable distortion or aberration. Quantitative mass detection experiments employing polystyrene microspheres further demonstrate that the device response increases linearly with the number of sample injections, and the shift magnitude is directly proportional to the amount injected per loading event. Full article

This article proposes a calibration method combining line chromatic confocal and 3D point cloud processing to solve surface damage and low efficiency in traditional roughness sample calibration. Line chromatic confocal sensors scan roughness samples to obtain dense point clouds. We propose a back projection mechanism, the adaptive density-based spatial clustering of applications with noise statistical outlier removal (BPM-ADBSCAN-SOR) algorithm that utilizes the ADBSCAN and SOR algorithms to address outlier noise and near-field noise in low-resolution point clouds, respectively, and then employs bounding boxes to crop the original high-resolution point cloud, thereby achieving multi-scale noise removal and point cloud clustering. We propose a Steady-State Confidence-Weighted Robust Gaussian Filtering (SSCW-RGF) algorithm, which calculates the range of the steady-state region, designs a steady-state region credibility weighting function to apply a weighted correction to the baseline fitting results, and then incorporates M-estimation theory to develop a robust Gaussian filtering algorithm weighted by steady-state region credibility, thereby mitigating the impact of outliers on Gaussian baseline fitting. Experiments verify the system accuracy: repeatability standard deviation is 0.0355 μm, relative repeatability error 0.3984%. Compared with reference block nominal values, the maximum absolute error is −0.745 μm, meeting specification tolerance. Compared with the contact profilometer, the maximum absolute error is 0.050 μm, the maximum relative error is +4.5%, and the calibration efficiency is improved by 90%. It provides a new approach for surface roughness calibration Full article

This article examines the attitude–behaviour gap in sustainable consumption among young adults in Poland, focusing on the psychological and contextual mechanisms that hinder the translation of pro-environmental attitudes into everyday practices. The findings show that declared support for sustainable solutions does not translate into behaviour when decision costs are elevated, particularly in terms of price, limited availability of green alternatives and low label readability. Drawing on six focus group interviews and reflexive thematic analysis, this study identifies the dominance of transactional over normative factors, a reliance on simple heuristics (e.g., packaging material) rather than verified certifications, and the central role of habit and convenience in consumption choices. Interpreting these results through the Theory of Planned Behaviour, the analysis shows how price- and supply-related barriers reduce perceived behavioural control and undermine sustainable decision making. This article contributes to behavioural research on sustainable consumption by identifying key mechanisms underlying the attitude–behaviour gap in a Central and Eastern European context. Practical implications include simplifying environmental labels, low-cognitive-load consumer education and interventions that increase the accessibility and visibility of sustainable options. Limitations stem from the qualitative design and the urban profile of the sample, suggesting cautious generalisation and the need for replication using diverse populations and methodological triangulation. Full article

The escalation of geopolitical tensions has forced global manufacturers to reconfigure their supply chains. While Digital Transformation (DT) is widely touted as a primary driver of resilience, traditional variance-based research often assumes a symmetric, linear relationship that applies universally across firms. This study challenges this assumption through the lens of Complexity Theory. Viewing supply chains as Complex Adaptive Systems (CASs), we employ Fuzzy-Set Qualitative Comparative Analysis (fsQCA) on a stratified sample of 928 manufacturers in a geopolitical high-risk zone (Taiwan). We identify equifinal pathways to Organizational Resilience, revealing a fundamental asymmetry between organizational types. The results suggest that while large enterprises rely on a resource-intensive strategy—which we term the “Digital Fortress” configurational metaphor (combining high digital maturity and agility as a core condition)—SMEs can achieve high resilience through an “Agile Dodger” configuration, leveraging operational agility and niche positioning without necessitating high digital maturity. This study contributes to the systems literature by mapping the “topology of resilience” and offering tailored configurational pathways that complement traditional variance-based perspectives in volatile ecosystems. Full article

Traditional vision-based vibration measurement is fundamentally constrained by the low sampling rates of area-scan cameras and the noise sensitivity of existing motion magnification algorithms. To overcome these spatiotemporal barriers, we propose a high-fidelity framework that integrates ultra-high-speed line-scan imaging with a 1D Complex Morlet Wavelet Phase-Based Video Magnification (CMW-PVM) algorithm. By extracting and manipulating the localized phase of 1D spatial signals, CMW-PVM effectively decouples structural dynamics from background noise while eliminating the computational redundancy associated with 2D spatial pyramid methods. Simulations demonstrate that CMW-PVM significantly extends the linear magnification range (up to $\alpha \approx 35$) while preserving exceptional structural fidelity (FSIM $>0.87$) under severe noise conditions (SNR = 10 dB). Experimental validation against a laser Doppler vibrometer (LDV) reveals near-perfect kinematic accuracy, with a relative amplitude error of only 1.65%. Furthermore, at a 100 Hz high-frequency excitation, the system successfully resolves microscopic displacements (≈10 μm) without temporal aliasing—enabled not by violating sampling theory but by leveraging the high physical line rate of the line-scan sensor. This establishes a robust, non-contact, and computationally efficient paradigm for broadband, micro-amplitude vibration monitoring in industrial environments. Full article

Finite Rate of Innovation (FRI) sampling has been widely used to sampling parametric signals at sub-Nyquist sampling rates. Nevertheless, real-world systems generally handle real-valued signals, posing challenges for acquiring complex domain Fourier coefficients directly. To overcome this limitation, we propose a Chebyshev polynomial-based FRI sampling framework that enables processing entirely in the real domain. Projecting the FRI signal onto the Chebyshev basis and employing a improved annihilating filter reformulates the parameter estimation problem into a classical spectral estimation task. Furthermore, the integration of the discrete Hilbert transform allows for a further reduction in both sampling channels and total sample count. Numerical simulations validate the effectiveness of the proposed approach and the generalizability of FRI theory across different signal bases. Full article

Understanding and managing tourism event experiences requires insight into how emotions unfold across space. Drawing on constructed emotion theory, this study advanced both theoretical and methodological knowledge by comparing three spatial emotion mapping approaches. Namely, we applied experience reconstruction maps, emotion physiology maps, and emotion effectiveness maps to visitor experiences of two events at nature-based fort settings in the Netherlands. Using intercept sampling (N = 98), we combined questionnaires with GPS tracking and skin conductance measurement to collect location-resolved emotional data. The resulting maps revealed markedly different spatial emotion patterns, reflecting distinct emotional components: physiological arousal and recalled or reconstructed emotional arousal and valence. Studies which map emotions have usually not accounted for within-individual autocorrelation. Our multilevel statistical models, in contrast, did account for this. The difference in emotion ranking of AOI raised questions about the validity of previous emotion mapping efforts. We extended constructed emotion theory to spatial analysis, demonstrated the limits of current mapping methods, and argued in favor of controlling for autocorrelation for robust spatial emotion research. Our findings can provide methodological guidance for researchers and event managers, and can lay the groundwork for future innovations that integrate physiological, self-reported, and spatial data for sustainable tourism event management. Full article

Entropic soft-min relaxations are widely used to obtain smooth approximations of minimum operators in optimization, machine learning, and control. The accuracy of this approximation is governed by an inverse temperature (or sharpness) parameter that controls the trade-off between smoothness and fidelity, yet its principled selection is typically heuristic. This work studies the data-driven calibration of the inverse temperature parameter governing the entropic soft-min relaxation, with explicit guarantees on the relaxation error between the soft-min operator and the infimum of the cost function. After establishing monotonicity properties and approximation bounds for the relaxation error, we introduce a conformal calibration rule that selects the smallest inverse temperature ensuring that the approximation error satisfies a prescribed tolerance with distribution-free finite-sample validity. The resulting selector adapts to the distribution of candidate cost-vector geometries represented in the calibration sample, enabling regime-specific inverse temperature selection in heterogeneous settings. Numerical experiments, including an adaptive cruise control application with safety filtering, show that the proposed method accurately tracks oracle calibration inverse temperatures and achieves near-target coverage in the exchangeable setting covered by the theory, while an additional shifted evaluation illustrates the role of this assumption. Full article

The use of one-step products and their applications in sensory applications has gained much importance. Herein, Schiff’s base fluorescent turn-on sensor, namely FBTS, was synthesised via a condensation reaction between 6-fluorobenzo[d]thiazol-2-amine and 2-hydroxybenzaldehyde. The probe FBTS exhibits an intense “turn-on” blue fluorescence upon binding to Al³⁺ ions in a dimethyl sulfoxide–water (DMSO–H₂O (8:2, v/v)) medium. From photoluminescence (PL) titrations, the detection limit (LOD) for Al³⁺ is estimated to be 0.14 microM, and the Benesi–Hildebrand plot-based association constant (K_a) of 5.4 × 10⁴ M⁻¹ confirm a strong association between FBTS and Al³⁺. Negligible interference is observed in the presence of other metal ions. From the pH effect studies, the optimal pH range for Al³⁺ detection is 7–9. The recyclable reversibility of FBTS + Al³⁺ complex has been demonstrated via the sodium salt of ethylenediaminetetraacetic acid (Na₂-EDTA) chelation. A Job’s plot and interrogations, such as high-resolution mass spectrometry (HR-MS), ¹H-nuclear magnetic resonance (NMR) titration, and density functional theory (DFT), verified the 1:1 stoichiometry of binding between FBTS and Al³⁺. Based on multiple analyses, the binding mode and mechanism have been detailed. In addition, the practical application of FBTS for detecting Al³⁺ is demonstrated using the strip paper method, fish analysis, spiked real sample analysis, and cellular imaging. Full article