Search Results (2,018)

This study examines whether the internal composition of an environmental, social and governance (ESG) rating contains incremental information about one-year accounting-loss onset after the overall ESG level is held constant. Bloomberg environmental (E), social (S) and governance (G) pillar scores are reparameterised as ESGLevel = (E + S + G)/3, GovernanceHalo = G − (E + S)/2, and ESContrast = E − S. The FY2020–FY2024 panel contains 13,745 firm-years for 2751 relatively large listed firms; the principal model uses 8029 eligible transitions and 472 loss-onset events. A one-standard-deviation increase in GovernanceHalo has an average marginal effect of −0.02 percentage points (95% CI −0.87 to 0.83). Firm-blocked cross-validation provides essentially no incremental one-year predictive gain. Reviewer-driven sensitivity analyses using within-year standardised pillars, a firm fixed-effects linear probability model, Firth penalised logit, stabilised inverse-probability weighting for complete-case selection, and dynamic pre-trend controls do not reveal a stable loss-onset association. A separate 30% annual market-capitalisation contraction outcome is negatively associated with GovernanceHalo and remains significant after false-discovery-rate adjustment (q = 0.024), but it is a valuation-contraction proxy rather than an accounting-loss or crash-risk measure. The previously observed E/S score build-out is no longer evident after controls for prior score changes, current E/S level and historical score volatility. The evidence therefore rules out large average one-year loss-onset effects in this Bloomberg-covered sample while leaving open smaller, heterogeneous, longer-horizon and provider-specific effects. Full article

Tm³⁺, Yb³⁺: Gd_xBi_2−xWO₆ series upconversion luminescent materials were synthesized via a solid-state method. Calculations and XRD analysis reveal that Gd³⁺ in the lattice exhibits both substitutional and interstitial doping. Compared with the NaYF₄ matrix, the Bi₂WO₆ material doped with Gd³⁺ has a lower Debye temperature (392.45 K), and the maximum phonon energy is low; thus, the probability of nonradiative transitions decreases. SEM images and EDS results reveal that the powder particle size ranges from 0.5 to 3 μm and that the elements are evenly distributed on the surface. The diffuse reflectance absorption spectra indicate that moderate Tm³⁺, Yb³⁺ and Gd³⁺ doping shifts the absorption band of the material towards longer wavelengths and decreases the band gaps. Under 980 nm laser excitation and a Gd³⁺ doping concentration of x = 0.2, the emission intensities of Tm³⁺ at 476 nm, 683 nm, 703 nm, and 804 nm increase by 1.8, 1.24, 1.35, and 81.9 times, respectively. Calculations reveal that these emissions in the range of 450 to 850 nm originate from three-photon or two-photon absorption. Gd³⁺ codoping can prolong the lifetime of the Tm³⁺ energy levels. When this sample was excited at 980 nm (518 mW), the temperature was characterized by FIR_{703 nm/476 nm} from 298 K to 573 K, and the obtained maximum relative sensitivity was 3.4907 K⁻¹ (298 K). The minimum temperature resolution was 0.0083 K (298 K). Compared with that of the undoped Gd³⁺ sample, the temperature sensitivity of the Gd³⁺-codoped sample increased by 92.35 times. Under an external magnetic field of 20 kOe and a temperature of 300 K, the magnetization of the sample doped with Gd³⁺ increased fourfold to 0.08174 emu/g. This material has potential applications in temperature sensing and magnetic resonance imaging. Full article

The Exponentially Correlated Hylleraas–Configuration Interaction method (E-Hy-CI) is a generalization of the Hylleraas–Configuration Interaction method (Hy-CI), in which the single \(r_{ij}\) is generalized to a form of the generic type \(r_{ij}^{\nu_{ij}}\text{e}^{-\omega_{ij}r_{ij}}\). This paper extends our earlier work of exploring whether wave functions containing both linear and exponential \(r_{ij}\) factors converge more rapidly. In the present study, we calculate the five lowest \(^1S\) and \(^1P\) states of He I in two wave functions. These two wave functions are used to calculate oscillator strengths, including upper and lower bounds, for the allowed transitions between these states. The results of this study are oscillator strengths for the \(n\) \(^1S\) \(\rightarrow\) \(m\) \(^1P\) transitions for \(n = 1-5\), \(m= 2-6\) with rigorous upper and lower bounds, which in the best case (1 \(^1S \rightarrow 2\ ^1P\)) are 0.00000001 % and probable precision ≤ 0.0000000001. Full article

We start from a pure vector update of the concepts of complex numbers and the characteristic function of a probability distribution, free from the imaginary unit, and update some basic properties of the latter. In particular, we derive vector inverse formulas for probability distributions and density functions, provide a new geometric proof of the convergence of suitably centered and normalized vector-powers of characteristic functions, and thus provide an update to the proof of the central limit theorem. Moreover, we demonstrate how to eliminate the imaginary unit from various equations—such as Schrödinger’s equation—by transitioning to vector equations. Full article

Carnitine palmitoyltransferase 1A (CPT1A) catalyzes a rate-limiting step in fatty acid β-oxidation and is a target for studies of metabolic disease and cancer. However, the structural dynamics of CPT1A remain poorly understood due to the lack of full-length structure. In this study, we employed an integrated computational approach combining molecular dynamics (MD) simulations, Markov state models (MSMs), and binding free energy calculations to elucidate the conformational landscape of human CPT1A. We performed an all-atom MD simulation of CPT1A embedded in a realistic outer mitochondrial membrane, and root-mean-square fluctuation (RMSF)-guided local region analysis coupled with MSM transition-path analysis and GROMOS clustering convergently identified a representative conformation. The pathway probabilities reported here describe dominant local transitions within specific mobile segments rather than the global equilibrium population of the full-length protein. Using this representative conformation, we performed a virtual screening of over 1.23 million compounds from the ChemDiv library, followed by Lipinski filtering, yielding five candidates with docking scores below −10 kcal/mol. Subsequent MD simulations and binding free energy calculations confirmed stable binding, with compound 2 exhibiting the most favorable free energy of −25.99 kcal/mol. Principal component analysis (PCA) and secondary-structure analyses revealed ligand-specific modulation of CPT1A dynamics. A preliminary in vitro CPT1 activity assay revealed candidate-specific responses, and the free-energy and activity data were partially consistent. These results provide a dynamically informed structural basis for CPT1A ligand recognition, identify candidates with distinct energetic and functional profiles, and establish an integrated strategy for ligand discovery in structurally uncharacterized proteins. Full article

In this review article, we first introduce the density matrix, along with its properties and
time evolution. We note that one of the principal uses of the density matrix is to calculate
measurement probabilities in quantum mechanics. When only some of the variables
can be measured, it is necessary to integrate out the variables that are inaccessible to
measurement. The resulting subsystem is then analogous to an open quantum system,
where the unmeasured variables belong to an unspecified environment. Coupled and
Lorentz-covariant harmonic oscillators are described and their differences are explained.
We introduce the density matrix for two harmonic oscillators and demonstrate how they
become entangled as well as entangled excited states of the Lorentz-covariant harmonic
oscillator. We elaborate on the concept of von Neumann entropy and show how entropy
increases when not all the variables of the system can be measured. We use the timeseparation
between quarks as an example of an unmeasurable quantity and then present
a discussion of von Neumann entropy and entanglement. We include “boiling quarksgluons”,
whose most interesting feature is the transition from the confinement phase to
the plasma phase, to illustrate the application of the density function and entropy. In this
context, we discuss the quark-parton puzzle and a possible explanation. Full article

Background: Recent advancements in early breast cancer (EBC) for high-risk populations have significantly improved invasive disease-free survival (IDFS). While cost-effectiveness has been assessed, the broader societal impact of improved IDFS remains unstudied. A non-homogeneous semi-Markov model was developed to estimate total healthcare and societal costs after first-line treatment with ribociclib plus an aromatase inhibitor (AI) or, alternatively, AI alone in patients with hormone-receptor-positive (HR+)/human epidermal growth factor receptor 2-negative (HER2-) EBC. Methods: Using an incidence-based approach, the model calculated total expected annual societal costs of disease-related events after cessation of adjuvant treatment with ribociclib plus AI or AI alone for HR+/HER2- EBC. The model includes five states: IDFS, non-metastatic recurrence (NMR), distant recurrence (DR), secondary primary malignancy (SPM) and death, with a one-year cycle length, and a new cohort entering each year. Health state transition probabilities are based on a previously published cost-effectiveness model (CEM). Costs are tallied only after IDFS (after cessation of adjuvant treatment) and include direct costs of medical care as well as indirect costs related to employment, productivity, and wage loss. Results: At the 4-year NATALEE data cut-off (April 2024), ribociclib plus AI reduced the risk of an IDFS event by 28.5% versus AI alone (HR 0.715), corresponding in the model to 23% fewer non-fatal events (NMR, DR, and SPM), 18% lower mortality, 23% lower total direct costs (−$957,471,465), and 24% lower indirect costs (−$448,542,692) from the period of cessation of adjuvant therapy until death. Healthcare savings were driven primarily by fewer distant recurrences and deaths, while societal savings were largely due to reduced caregiver costs. Conclusions: Treatments that reduce disease recurrence and mortality can significantly lower societal costs and alleviate burden on the healthcare system. Full article

Autonomous agents in adversarial environments must allocate sensing resources, deploy spatial assets, and control combat actions under partial observability. We formulate this coupling as a three-phase partially observable decision process with heterogeneous action spaces. Strategic Planner provides a structured, non-recurrent policy that combines a shared phase-conditioned representation, masked entity attention, a six-channel tactical-map encoder, static plan tokens, competitive entity–map weighting, and phase-specific heads. Configuration uses a probability-consistent tanh-squashed Gaussian for 11 bounded controls and three categorical choices; deployment and battle use legality-masked categorical distributions. We evaluated the architecture against sequence-aware PPO-GRU and non-recurrent baselines under matched environment-transition budgets. Across eight training seeds, the deterministic win rate was 21.8 ± 28.0% for Strategic Planner and 25.8 ± 22.7% for PPO-GRU; their paired difference was $-4.0$ percentage points (95% bootstrap interval $[-21.8,18.1]$). Strategic Planner had numerically close mean episode reward (78.0 versus 77.5) and lower mean Command Point cost (753.5 versus 775.9), but paired intervals do not establish an advantage on these endpoints. Peak batch-one GPU allocation was 49.1 versus 54.6 MiB. Three-seed component ablations were inconclusive, and shifted-map outcomes varied. The study delivers an auditable, probability-consistent phase-coupled architecture and a quantified account of its trade-offs with recurrent memory. Full article

Global navigation satellite system (GNSS) navigation-measurement spoofing is a critical threat to autonomous unmanned aerial vehicles (UAVs) because deceptive measurements may remain numerically plausible while progressively corrupting the navigation state. This paper presents an integrity-aware hierarchical navigation framework designed to maintain mission continuity under GNSS spoofing and subsequent degradation of the LiDAR–inertial fallback. During nominal operation, GNSS is cross-validated against an independent LiDAR–IMU error-state Kalman filter solution using normalized innovation squared (NIS), persistence logic, and cumulative change evidence. Suspect GNSS measurements are quarantined, after which LiDAR–inertial navigation maintains local continuity while a secondary integrity monitor evaluates innovation consistency, estimator uncertainty, feature quality, and inertial plausibility. If the fallback loses integrity, the UAV transitions to a map-referenced visual recovery layer that combines altitude-constrained Google Earth tile selection, deep cross-view retrieval, geometric verification, and visual SLAM. The experiments used controlled navigation-measurement spoofing introduced in the ROS 2 navigation path rather than radiated RF spoofing. In the replay-based detector analysis, the complete detector achieved an AUC of 0.984, a detection probability of 0.963, and a mean gradual-attack detection latency of 2.25 s (standard deviation 0.68 s). These descriptive results were obtained from repeated replay instances derived from 20 source missions. Under sequential GNSS and LiDAR–inertial degradation, the proposed integrity manager achieves 95% recovery success in the evaluated campaign, reduces the full sequential-mission 95th-percentile horizontal position error from 5.31 m to 2.31 m relative to hard switching, and obtains an accepted visual fix in 1.92 s (standard deviation 0.67 s). End-to-end horizontal localization RMSE is 1.39 m, while bounded state reconciliation reduces the peak commanded-state jump by 89.1% and post-recovery cross-track RMSE by 71.4%. These results support explicit integrity monitoring and controlled transitions between navigation sources under sequential sensor degradation. Full article

The Condorcet paradox is traditionally viewed as a macro-level phenomenon of collective decision-making, showing that individually rational agents with coherent and transitive preferences may nevertheless generate socially intransitive majority outcomes. This interpretation assumes that cyclicity emerges exclusively through aggregation. In this study, we distinguish between micro-Condorcet dynamics, arising from contextual interactions within individual decision processes, and macro-Condorcet dynamics, emerging through majority aggregation. To investigate the relationship between these two levels, we introduce a minimal contextual model in which pairwise decisions are generated sequentially and linked through a contextual coupling mechanism. Although individual agents remain locally coherent and entirely classical at the microscopic level, each decision modifies the context of subsequent comparisons. Simulation results show that increasing contextuality progressively increases the probability of both micro- and macro-level Condorcet cycles. The transition between the two levels is strongly influenced by heterogeneity in decision thresholds, particularly those governing intermediate and closing comparisons. Rather than being driven primarily by initial preference relations, collective cycles emerge from heterogeneous evaluations of intermediate alternatives, which amplify contextual effects and facilitate cycle closure. A critical contextuality threshold is observed beyond which classical representability breaks down and no probability distribution over transitive preference rankings can reproduce the observed pairwise preference probabilities. These findings suggest that quantum-like probabilistic structures may emerge naturally as higher-level representations of contextual collective decision systems. Full article

The large-scale integration of intermittent renewable energy has increased operating-condition transitions in pumped storage hydropower plants, thereby raising the probability of successive load rejection (SLR) and associated hydraulic transient risks. To mitigate the severe low draft tube pressure (DTP) commonly induced by SLR, this study proposes installing a tailwater connection pipe (TWCP) between the draft tubes of adjacent units. The mitigation mechanism and performance are investigated through theoretical modeling and three-dimensional numerical simulations. Results show that the first load-rejecting unit (FLRU) enters the reverse-pump region prematurely and effectively behaves as a normal-rotation pump. This accelerates the discharge reduction in the subsequent load-rejecting unit (SLRU), drives its operating point deeper into the reverse-pump region, and substantially worsens its minimum DTP. With the TWCP, the head difference drives compensating flow from the FLRU to the SLRU, alleviating transient water shortage, reducing discharge and pressure discrepancies between the two tailwater systems, and improving local turbulent flow. The mitigation effect is sensitive to structural parameters: a larger pipe diameter increases flow capacity, while installation closer to the pump-turbines increases the driving head difference. Both measures enhance DTP mitigation, whereas the TWCP has little influence on steady-state flow conditions. Full article

Depressive symptoms in later life develop alongside changes in physical function and contact with healthcare services, but average associations do not show how these features combine within individuals over time. We analyzed 6229 adults aged 60 years or older who participated in the 2015, 2018, and 2020 waves of the China Health and Retirement Longitudinal Study. Missing item data were handled with 20 multiply imputed datasets. Robust mean-shape latent growth models summarized population-average change in a six-component ADL score, a three-component healthcare-contact count, and CES-D-10 depressive-symptom scores. Functional status, healthcare contact, and depressive-symptom status were then combined into 18 joint states, with transition probabilities estimated separately for 2015–2018 and 2018–2020. Participant-level bootstrap resampling and Rubin pooling were used for interval-specific risks and prediction comparisons. ADL scores declined slightly, whereas healthcare-contact counts and CES-D-10 scores increased; the latter two changes were not constant across the two intervals. Next-wave depressive-symptom risk was driven mainly by prior depressive symptoms and was higher in functionally impaired states. The full transition distributions differed between intervals, although risk rankings among well-populated origin states were similar. Population-weighted analyses showed that common healthy-function pathways contributed more observed depressive-symptom cases than rarer severe-impairment pathways despite lower conditional risks. In temporal-transfer analysis, the full 18-state model improved AUC relative to a covariate-only model, but added little beyond prior depressive-symptom status. The multistate representation is therefore most useful for describing heterogeneous transition patterns and separating state-conditional risk from population pathway contribution, rather than as a stand-alone prediction algorithm. Full article

Closest Point of Approach (CPA) is widely used in surveillance-based collision-risk monitoring to estimate the time and minimum lateral and vertical separation between projected aircraft trajectories over a bounded future horizon. Deterministic CPA performs this calculation from the current position, speed, and heading of each aircraft, making it interpretable and computationally efficient. However, in terminal airspace, where aircraft frequently turn, straighten, and capture approach paths, a single projected trajectory may not adequately represent plausible short-term heading evolution. This study develops a probabilistic extension of bounded-horizon CPA, denoted probabilistic P-CPA, that preserves the familiar CPA-family outputs while estimating the probability that a predefined safety volume is entered under short-term heading uncertainty. State-dependent maneuver-transition probabilities and heading-change distributions were learned from historical trajectories and propagated through Monte Carlo simulation, with the assessment recomputed at each synchronized 5 s observation. The method was evaluated using six months of arrivals to San Francisco International Airport runways 28L and 28R. The results show that probabilistic P-CPA identifies scenarios in which deterministic CPA outcomes are sensitive to plausible short-term heading evolution, particularly when aircraft are entering, continuing, or exiting turns. Implications of this capability, limitations, and future work are discussed. Full article

Predefined educational content provides curricular control and consistent quality but remains non-personalized when all learners receive the same sequence and amount of practice. This study proposes Fuzzy Adaptive Revision with Bayesian Knowledge Tracing (FAR-BKT), a framework based on the assumption that educational items assessing the same or closely related knowledge can share learner evidence while retaining separate question-level mastery estimates and revision decisions. FAR-BKT extends fixed-parameter BKT through three adaptive mechanisms: a dynamic learning-transition probability $p\left(T\right)$, a context-sensitive slip probability $p\left(S\right)$, and an adaptive fuzzy mastery threshold $\theta$. The transition probability is adjusted based on group-level response accuracy, $p\left(S\right)$ from option confusability and learner error history, and the threshold determines whether individual questions remain active for further revision. Six matched policies were evaluated using 1000 heterogeneous synthetic learners, 24 questions, and eight knowledge groups. Dynamic $p\left(T\right)$ produced the greatest standalone improvement. FAR-BKT achieved the highest mastery coverage (0.462), lowest final estimation MAE (0.167), and highest removal precision (0.453), but required 11.1% more attempts than fixed BKT. The findings demonstrate an interpretable effectiveness–effort trade-off and provide computational, rather than classroom, evidence. Full article

The development of automatic fingerspelling recognition systems is critical for facilitating seamless communication for the deaf and hard-of-hearing community. Historically, recognition technologies relied on wearable sensors, such as data gloves, inertial measurement units (IMUs), and surface electromyography (sEMG), which provided stable kinematic data but suffered from high hardware costs and user discomfort. Non-invasive vision-based sensing has emerged to eliminate wearable overhead but is subject to severe physical bottlenecks: continuous high-speed movements inevitably introduce motion blur and self-occlusion, drastically degrading tracking accuracy. To address these visual sensing constraints, we investigate a visual-language integration framework that combines dynamic visual representations derived from a single RGB video stream with the lexical knowledge of Large Language Models (LLMs), allowing the language model to compensate for uncertainty or errors in the visual representation. Our approach employs a robust visual encoder—comprising a Temporal Convolutional Network (TCN) and a Transformer—to extract and integrate dynamic transition features from 3D skeletal coordinates. Rather than propose a new LLM architecture, this study focuses on how visual information should be represented and transferred to an LLM by comparing discrete-text, probability-level, and latent-feature-level integration strategies under the same experimental conditions. Specifically, we systematically evaluated three integration strategies: Text Input (TI), Projector Probabilities Input (PPI), and Projector Transformer Features Input (PTFI). Experimental results on a 28-word closed-set evaluation show that while the word-level exact-match accuracy (WA) of the visual encoder alone was limited to 11.66% with continuous signing, our LLM-integrated architecture elevated the WA to a maximum of 87.73%. Crucially, the PTFI approach successfully suppressed character-level degradation by grounding linguistic reasoning in latent Transformer-based visual evidence. These results suggest that integrating LLM-based linguistic reasoning with dynamic visual features is a promising approach for closed-set continuous fingerspelling recognition under controlled experimental conditions. Full article