Search Results (336)

The proposed methodology combines analytical control laws with adaptive mechanisms and machine-learning-assisted modules based on regression trees, random forests, and extreme gradient boosting (XGBoost). Machine learning models are employed to approximate unknown nonlinear dynamics, compensate disturbances, and adjust controller parameters, while the overall control structure is constrained by Lyapunov stability conditions. This ensures that the inclusion of data-driven components does not violate the fundamental requirement of system stability. The effectiveness of the proposed approach is evaluated through simulation experiments across three operating modes with varying degrees of nonlinearity and dynamic complexity. The results show that hybrid models incorporating ensemble machine learning methods improved performance compared with the analytical and adaptive baselines examined. XGBoost-based control achieves the lowest error values and the highest level of Lyapunov stability compliance (up to 99.3%). The main contribution of this study lies in the development of a unified synthesis framework in which machine learning is not used as a standalone control strategy but as a machine-learning-assisted support mechanism integrated into a theoretically grounded control architecture. The proposed approach provides a balance between adaptability, accuracy, and rigorous stability guarantees, suggesting potential applicability to simulation-based and offline-assisted control design tasks, while real-time embedded implementation requires additional computational optimization and validation. Full article

The study examines judicial practice regarding claims for damages and non-pecuniary damages (hereinafter: NPDs) arising from violations of the duty to inform in healthcare by comparing two periods (2008–2010 and 2018–2020) in the context of patient self-determination and European trends in patient rights. The 193 final judgments selected from the Wolters Kluwer Law Database based on keyword searches underwent qualitative content analysis and quantitative processing using SPSS (Statistical Package for the Social Sciences, SPSS version 25.0). A selection criterion was that the judgment should assess on its merits whether the duty to inform had been fulfilled or violated. The real value of the adjudged compensation was compared and normalized in relation to the minimum wage (multiplied by the minimum wage) in order to reveal the actual socio-economic weight of the compensation. The results show that while in 2008–2010, the lack of information was mostly considered an additional element of professional negligence, by 2018–2020, it was recognized as a separate violation of personality rights that infringed on the right to self-determination, and the rate of complete rejection of claims for NPDs decreased. However, the increase in nominal amounts was accompanied only to a limited extent by an increase in the real value of compensation. The findings suggest that Hungarian judicial practice is moving closer to the autonomy-centred European approach, while strengthening the reparative function of NPDs—ensuring compensation that is perceptible in real terms—remains an open task. Full article

This online survey aimed to elaborate on the salary, working conditions, and job satisfaction of employed veterinarians in Germany. The focus was on factors influencing salaries, violations of German laws, and comparisons between employees of owner- and corporate-managed practices. Answers of up to 1184 veterinarians were analyzed, representing 6% of employed veterinarians. The hourly salary increased by around 19% compared to a study in 2020, but remained significantly lower than in comparable professions and did not rise as much as the national average over the last five years. A multifactorial linear model showed that working experience, additional qualifications, leadership role for other veterinarians, section (pets, equines, farm animals, or non-curative), and gender significantly influenced the salary. The adjusted gender pay gap was about 7%. Employees of corporations earned significantly more than veterinarians being employed in owner-managed practices, but not when salary was adjusted for overtime. Moreover, employees of corporations had significantly lower job satisfaction. Requirements of the German Working Hours Act were regularly not complied with, as e.g., around 40% of respondents reported not being able to take their legally required break at least once per week. Results indicate that, despite improvements, there is still a need to address working conditions to retain veterinarians in the profession. Full article

Although Graph Neural Networks (GNNs) are widely regarded as an ideal tool for capturing spatial dependencies in river basins, their effectiveness in hydrological forecasting is severely challenged by a topology paradox: under a purely data-driven paradigm, GNNs fail to spontaneously learn physical laws, generating predictions that lack physical interpretability and frequently violate mass conservation. To address this fundamental problem, this paper proposes a physics-informed graph learning framework integrated with an explicit, differentiable water balance constraint (WB-GNN). By reconstructing the continuity equation into a differentiable loss function, we directly embed physical conservation as a strong inductive bias into the neural network’s training objective. We comprehensively evaluated the model on two large-sample datasets (LamaH-CE and CAMELS) against state-of-the-art baselines, including EA-LSTM and unconstrained Pure-GNN. Quantitative results demonstrate that the proposed physical constraint successfully awakens the potential of river network topology. On the LamaH-CE dataset, WB-GNN achieved a Nash-Sutcliffe Efficiency (NSE) of 0.86 and a Root Mean Square Error (RMSE) of 9.2 ${\mathrm{m}}^3/\mathrm{s}$, outperforming both the domain-specific EA-LSTM (NSE: 0.83) and the unconstrained Pure-GNN (NSE: 0.74). Crucially, the introduction of the differentiable constraint reduced the Physical Inconsistency Ratio (PIR) by an order of magnitude-from 39.8% in the unconstrained model to just 4.3%. Similar robust improvements were validated across the highly heterogeneous CAMELS dataset. These quantifiable results confirm that the proposed method not only achieves superior forecasting accuracy but also fundamentally guarantees physical trustworthiness, making it highly robust for critical decision-making in extreme flood events. Full article

We introduce a local phase-field framework for spin-entanglement correlations. In this framework, the relevant hidden variable is an internal scalar phase associated with each fermion and derived from two underlying real fields. The fields are assumed to evolve locally in ordinary spacetime. When a particle pair is produced at a common spacetime event, the pair acquires a shared phase-locking condition at creation; after separation, the two internal phases evolve independently and no nonlocal interaction is introduced. Spin measurements by Stern–Gerlach analyzers are modeled as local filtering operations. Each local response depends only on the internal phase carried by the particle and on the orientation of the local analyzer. The local response function A(α,λ) = cos(λ − 2α) is derived from the spinorial transformation law of the underlying real field pair and the projection geometry of the detector interaction; it is not a phenomenological ansatz. From these deterministic local responses we derive an analog correlator. The raw product moment of the continuous detector outputs evaluates to ⟨AB⟩ = −½ cos 2(α − β), which satisfies classical Clauser-Horne-Shimony-Holt (CHSH) bounds. After Pearson normalization—the operationally appropriate correlation measure for continuous analog detector outputs, justified by channel-contrast physics and scale invariance—the normalized correlator yields E(α,β) = −cos 2(α − β), matching the quantum singlet correlator in functional form. When this normalized correlator is inserted into the CHSH expression, it yields the numerical value 2√2. This result is a structural consequence of the reduced marginal variance of continuous response functions relative to the unit-variance dichotomic observables assumed in Bell’s derivation; it does not constitute a violation of Bell’s inequality. The model does not reproduce quantum singlet statistics at the level of binary detector outcomes, where the correlator takes a triangular rather than cosine form. The contribution is therefore ontological and conceptual rather than predictive. The framework preserves parameter independence and no-signaling throughout. It provides a concrete real-field ontology for spin correlations based on internal phase structure, and it demonstrates that the functional form of the quantum singlet correlation can be obtained from a strictly local deterministic description, provided that the detector responses are treated as continuous analog quantities and normalized accordingly. We compare the model with earlier phase-based approaches and discuss experimental configurations—including time-resolved and multi-stage Stern–Gerlach measurements—that could in principle probe the proposed internal-phase dynamics at the pre-registration level. Full article

Aiming at the problems of the large prediction error of model-driven algorithms and poor interpretability (even potential violation of physical laws) of pure data-driven algorithms in the prediction of aerospace vehicle plume characteristics, a physics mechanism-guided prediction algorithm for aerospace vehicle plume characteristics was proposed. Taking the long short-term memory (LSTM) network as the backbone, this algorithm constructed a hybrid physics–data model by embedding the prior knowledge of physical laws and empirical rules into the neural network, and designed a loss function combined with physical mechanisms to guide network training. The aerospace vehicle plume dataset was preprocessed through characteristic parameter extraction, extended physical parameter calculation, data splicing and sliding window operation, and the LSTM network structure was optimized by adjusting hyperparameters such as the number of hidden layers and neurons. Experimental results show that the proposed algorithm achieves a Mean Absolute Error (MAE) of 31.89 and a Physical Inconsistency of 0.1723 on the test set, with MAE reduced by 14% and Physical Inconsistency reduced by 7.5% compared with traditional machine learning models such as Random Forest. Ablation experiments verify that the introduction of physical mechanisms can improve the prediction accuracy of the model by about 25%. This algorithm makes up for the defects of traditional prediction algorithms, has good generalization ability and physical consistency, and provides an effective method for the prediction of engine exhaust plume temperature distribution. Full article

In this paper we study photon entanglement in the framework of Maxwell–Schwartz field theory. The ambient state space is the complex Maxwellian distribution space $W={\mathcal{S}}^{\prime }({\mathbb{M}}_4,{\mathbb{C}}^3)$, whose elements are fields of the form $F=\overrightarrow{E}+ic\overrightarrow{B}$. Polarization is realized as a two-dimensional complex subspace of W, generated by suitable linearly polarized Maxwellian solutions associated with opposite propagation directions. This yields canonical polarization sectors $P_A$ and $P_B$, each naturally isomorphic to ${\mathbb{C}}^2$. Within this setting, the Bell singlet state is represented by a non-factorizable tensorial Maxwellian field in $P_A\otimes P_B\subset W\otimes W$. By means of the induced rotated polarization bases, the standard joint probabilities of the photon polarization experiment are recovered exactly, and the correlation law $E(a,b)=-cos\left(2\right(a-b\left)\right)$ is obtained. Consequently, the usual CHSH value $2\sqrt{2}$ is reproduced in the Maxwell–Schwartz framework. To clarify the meaning of this violation, we first formulate the CHSH inequality in a purely measure-theoretic form, as a theorem about four correlators represented on a single probability space by bounded measurable functions. We then show that the correlators produced by the intrinsic Maxwellian Bell state do not admit such a common representation. The obstruction is structural: the ontic state is a global non-product field configuration, and the four correlations arise from different polarization resolutions of the same tensorial Maxwellian state. A second main result concerns the Born rule. For $L^2$ scalar quantum states in the domain of the Maxwellian correspondence, we prove that the squared Hilbert norm, times the constant ${\epsilon }_0$, coincides with the electromagnetic energy of the associated field. This leads to an energy interpretation of the Born rule: the Born probability density is identified with the normalized electromagnetic energy density up to an interference term depending on the chosen Maxwell–Schwartz isomorphism, which assumes the role of a quantum context. In the context of the Aspect and collaborators’ experiment, we prove that, on the other hand, the polarization probabilities become energy contributions of the corresponding field components. These results show that photon entanglement, Bell inequality violation, and the Born rule admit a coherent interpretation within Maxwell–Schwartz field theory, where the basic ontological objects are electromagnetic-like fields rather than abstract state vectors. Full article

Conventional tests of Bell’s inequality rely on entangled photon pairs. Here, we replace entangled pairs with two independent photons of orthogonal polarization and demonstrate that Bell’s inequality is still violated. Given the inherent local realism of independent photons, this experiment proves that Bell’s inequality cannot falsify the local realism of photons. We thus conjecture that the violation of Bell’s inequality by entangled photon pairs originates from their orthogonal polarizations rather than the breakdown of local realism. To interpret this unexpected violation with independent photons, we further substitute the two photons with two monochromatic light beams and calculate the transmittance correlation through polarizers via Malus’s law and Karl Pearson’s correlation formula. We show that this correlation also defies Bell’s inequality. Retracing the derivation of Bell’s inequality reveals that its validity is restricted to binary events, which accounts for the observed violation with light beams. Finally, we propose a thought experiment involving the gradual attenuation of light intensity down to the single-photon regime and hypothesize that single-photon transmission through a polarizer does not constitute a binary event. This hypothesis provides a unified interpretation for both our experimental findings and all canonical Bell inequality tests reported to date. Full article

Globally, the effects of widowhood on the welfare, health, financial security and education of the widow’s children in many contexts have been the subject of much research. This paper aims to uncover the nexus among culture, gender and language on widowhood dignity and welfare among four chosen African ethnic groups in South Africa and Zimbabwe. The widowhood phenomenon is culture-bound and value-laden as it signposts the reality of existence in the linguistic and cultural contexts in which it is created and operationalised. Through Kimberlé Crenshaw’s 1989 intersectional theory, this paper provides an in-depth, inductive qualitative investigation of the implications of culture, gender, language, and especially the nomenclature that African communities ascribe to the widowed, which in turn stigmatises widowhood. Two (2) South African and two (2) Zimbabwean ethnic groups were purposefully chosen for the multiple case study approach. Grounded theory is the coding framework and analysis technique. The coding starts off with picking key words, phrases and sentences and axial coding which is a higher level in which related data are grouped into sub-themes, themes and global themes. The search revealed that widowhood language, culture and nomenclature denote gendered, culturally contested spaces in which the widowed women especially face dehumanising and dewomanising rituals. The results gathered fall into five broad categories, namely, sexualised widowhood mourning rituals, psychological and emotional widowhood torture rituals, ritualised widowhood dispossession, swearing, movement and space restriction widowhood rituals. The rituals affirm the ascribed socially depressed widowed status implied in the stigmatising nomenclature. The paper recommends redefining widowhood in terms of humanising and womanising language, cultural rituals and nomenclature in the context of equality before the law. Such a move prevents discrimination against the widowed that unintentionally violates their constitutionally espoused right to equality. Full article

Rapid urbanization in megacities has exacerbated the spatial mismatch between employment and housing, necessitating effective spatial optimization strategies. However, classical optimization models often rely on the idealized assumption of “proximity maximization,” failing to account for the complex, nonlinear regularities of actual human mobility. To address this disconnect between theoretical modeling and real-world behavior, this study establishes a job–housing balance optimization framework integrated with empirical commuting patterns. Using Shenzhen as a case study, we analyze citywide commuting big data since 2024 to characterize the power law relationship between commuting population size and distance. We propose a novel optimization model that partitions residential areas into “commuting rings” on the basis of observed distance-decay functions rather than simple Euclidean proximity. We applied the proposed method to current and future planning scenarios and successfully generated spatial regulation schemes that decentralize employment functions to peripheral areas while strategically densifying residential zones. By respecting the “heavy-tailed” nature of commuting distributions, this approach offers urban planners a more robust tool for reducing aggregate commuting burdens without violating the behavioral realities of the workforce. Full article

Over the past five years, the number of new United States laws banning gender-affirming care, restricting public access to services and spaces for transgender and gender-diverse persons, and forcibly outing transgender youth in schools has increased dramatically. Much of the focus in the media and research has been on the domestic political and social causes of these anti-transgender and anti-gender-affirming care laws and their devastating effects on vulnerable transgender and gender-diverse communities. This article argues that the current wave of anti-transgender and anti-gender-affirming care laws violates civil and human rights in the context of international human rights resolutions and principles on healthcare and displacement. I explore the implications of using international human rights to challenge anti-transgender and anti-gender-affirming care legislation and what coalitional possibilities exist when expanding the fight against these laws transnationally. Full article

Background: Firearm injuries are the leading cause of mortality among youth in the United States and legislation is a key strategy in reducing youth firearm injuries and deaths. Maryland recently enacted a stronger child access prevention (CAP) law known as Jaelynn’s Law, which mandates secure firearm storage and imposes stricter penalties for violations. Objectives: The aim of this study was to examine firearm storage practices and beliefs in a pediatric and adult emergency department in Baltimore before and after the implementation of Jaelynn’s Law. Method: This descriptive study recruited 396 adult participants from pediatric and adult EDs at Johns Hopkins Hospital before and after the implementation of Jaelynn’s Law. Participants completed a survey on demographics, firearm ownership, and storage practices. Those with unsafe storage practices were provided educational pamphlets and safe storage devices. Data were analyzed using SPSS Statistics 28, with descriptive statistics, t-tests, and Chi-square analyses used to assess differences pre- and post-law implementation. Results: Of the participants, 29% owned firearms, with 86% of firearm owners having children in the home. Firearms were primarily stored locked and unloaded. No significant differences in storage practices were observed after implementation of Jaelynn’s Law. Participants cited quick access for personal protection as a key barrier to safe storage. Conclusions: We found no significant change in safe storage practices post-implementation of Jaelynn’s Law. Concerns about personal safety continue to be of primary concern and public health campaigns, legislative measures, and community investment are necessary to enhance safety and safe storage compliance. Full article

Traffic flow forecasting provides essential support for the construction of smart transportation systems. Despite the superiority of the ASTGCN, which uses an attention mechanism to capture spatio-temporal correlations, it lacks an explicit physical interpretation and thus falls into a more general category known for its lack of such interpretation. As a result, in the presence of sparse or unstable congestion, these data-driven models often violate conservation laws and may generate “physical anomalies” or other logically impossible states. To close the gap of data-driven expressiveness and physical consistency, we propose the congestion-aware physics-guided STGCN (CAP-STGCN). This framework builds a synergistic model that achieves intrinsic coupling between the macroscopic traffic flow kinematics (fundamental diagram) and the spatio-temporal learning process. That is to say, under the model’s solution-space constraining effect, its motion space is bound on a feasible manifold. In terms of kinematics, it restricts consistency in the flow, density and speed. Concurrently, to address slow convergence under long-tailed distributions due to a lack of training samples, such as when there are fewer users or higher-quality items, a dynamic congestion-rectification mechanism is introduced. The aforementioned mechanism redefines the optimization landscape by prioritizing hard-to-predict saturation occurrences. Experiments show that, compared with other models, CAP-STGCN achieves higher prediction accuracy; more importantly, it is free of physical anomalies during inference and can be directly used in practice. Full article

Hydraulic manipulators face critical challenges due to valve dead-band nonlinearity and state constraints, which can lead to safety hazards and hardware damage. This study proposes a state-constrained controller with valve dead-band compensation to ensure prescribed positioning accuracy and operational safety. Barrier Lyapunov functions ensure that state constraints are maintained and that boundary violations are avoided. Concurrently, a smooth dead-band inverse model is developed to offset asymmetric valve dead-band effects without inducing chatter. Adaptive laws estimate uncertain parameters and dead-band impact in real time, and a disturbance observer attenuates unmatched uncertainties. Dynamic surface control is employed to diminish the explosion of complexity in backstepping design. Comparative simulations under fixed-angle and arbitrary-angle tracking demonstrate that the proposed controller achieves superior tracking accuracy with steady-state errors below 0.04° compared to 0.06° for non-compensated controllers, while significantly reducing pressure fluctuations and control chattering as adaptive parameters converge. The results indicate that the strategy effectively compensates for valve dead zones while strictly maintaining state constraints, thereby achieving the required control precision for hydraulic servo systems. Full article

A neural network simulation–regression method was developed to assess the impact of sanctions on the level of administrative offences under fragmented, noisy, and short administrative time series. The study addresses the problem of quantifying and predicting changes at the offence level as a sanction size function, using detection probability, prior violation level, compliance costs, and auxiliary contextual factors. The proposed framework combines a hybrid MLP–LSTM neural network, double machine learning-based orthogonal causal estimation, the simulation-based generation of counterfactual scenarios through domain randomization, multiple imputation for missing data, debiasing procedures, and ensemble uncertainty estimation. The contribution to administrative law consists of a quantitative tool creation for substantiating and optimising sanction policy, assessing heterogeneous effects, and supporting evidence-based rulemaking and law enforcement decisions. In comparative experiments, the developed method achieved an RMSE of 8…12%, a prediction accuracy of 93…96%, an overall accuracy of 95%, a precision of 94%, a recall of 93%, and an F1-score of 93.5%, thereby outperforming contemporary econometric, simulation, causal machine learning, and predictive machine learning approaches used for sanction effect modelling. Additional verification through nonparametric statistical testing confirmed that the proposed model’s superiority over the compared algorithms is statistically significant across the main quality metrics, which strengthens the evidence for its robustness and practical value in sanction policy analysis under fragmented administrative data conditions. Full article