Search Results (75,014)

We consider mixed Hamming packings, addressing the maximal cardinality of codes with a minimum codeword Hamming distance. We do not rely on any algebraic structure of the alphabets. We extend known-integer linear programming models of the problem to be efficiently tractable using standard ILP solvers. This is achieved by adopting the concept of contact graphs from classical continuous sphere packing problems to the present discrete context, resulting in a reduction technique for the models which enables their efficient solution as well as their decomposition to smaller subproblems. Based on our calculations, we provide a systematic summary of all lower and upper bounds for packings in the smallest Hamming spaces. The known results are reproduced, with some bounds found to be sharp, and the upper bounds improved in some cases. Full article

Knowledge Graph Reasoning (KGR) aims to deduce missing or novel knowledge by learning structured information and semantic relationships within Knowledge Graphs (KGs). Despite significant advances achieved by deep neural networks in recent years, existing models typically extract non-linear representations from explicit features in a relatively simplistic manner and fail to fully exploit semantic heterogeneity of relation types and entity co-occurrence frequencies. Consequently, these models struggle to capture critical predictive cues embedded in various entities and relations. To address these limitations, this paper proposes a relation aware spectral decoupling attention network for KGR (RASD). First, a spectral decoupling attention network module projects joint embeddings of entities and relations into the frequency domain, extracting features across different frequency bands and adaptively allocating attention at the global level to model frequency specific information. Next, a relation-aware learning module employs relation aware filters and an augmentation mechanism to preserve distinct relational properties and suppress redundant features, thereby enhancing representation of heterogeneous relations. Experimental results demonstrate that RASD achieves significant and consistent improvements over multiple leading baseline models on link prediction tasks across five public benchmark datasets. Full article

This paper investigates a novel class of weighted fuzzy fractional Volterra–Fredholm integro-differential equations (FWFVFIDEs) subject to integral boundary conditions. The analysis is conducted within the framework of Caputo-weighted fractional calculus. Employing Banach’s and Krasnoselskii’s fixed-point theorems, we establish the existence and uniqueness of solutions. Stability is analyzed in the Ulam–Hyers (UHS), generalized Ulam–Hyers (GUHS), and Ulam–Hyers–Rassias (UHRS) senses. A modified Adomian decomposition method (MADM) is introduced to derive explicit solutions without linearization, preserving the problem’s original structure. The first numerical example validates the theoretical findings on existence, uniqueness, and stability, supplemented by graphical results obtained via the MADM. Further examples illustrate fuzzy solutions by varying the uncertainty level (r), the variable (x), and both parameters simultaneously. The numerical results align with the theoretical analysis, demonstrating the efficacy and applicability of the proposed method. Full article

Clarifying the spatiotemporal relationship between urban ecosystem services and changes in landscape patterns is essential, as it has significant implications for balancing ecological protection with socio-economic development. However, existing studies have largely focused on the one-sided impact of landscape patterns on either the supply or demand of ESs, with limited investigation into how changes in these patterns affect the growth rates of both supply and demand. The central urban area, characterized by complex urban functions, intricate land use structures, and diverse environmental challenges, further complicates this relationship; yet, the spatiotemporal differentiation patterns of ecosystem services’ supply–demand dynamics in such regions, along with the underlying influencing mechanisms, remain insufficiently explored. To address this gap, the present study uses Shenyang’s central urban area, China as a case study, integrating multiple data sources to quantify the spatiotemporal variations in landscape pattern indices and five ecosystem services: water retention, flood regulation, air purification, carbon sequestration, and habitat quality. The XGBoost model is employed to construct non-linear relationships between landscape pattern indices and the supply–demand ratios of these services. Using SHAP values and LOWESS analysis, this study evaluates both the magnitude and direction of each landscape pattern index’s influence on the ecological supply–demand ratio. The findings outlined above indicate that: there are distinct disparities in the spatiotemporal distribution of landscape pattern indices at the patch type level. Additionally, the changing trends in the supply, demand, and supply–demand ratios of ecosystem services show spatiotemporal differentiation. Overall, the ecosystem services in the study area are developing negatively. Further, the impact of landscape pattern characteristics on ecosystem services is non-linear. Each index has a unique effect, and there are notable threshold intervals. This study provides a novel analytical approach for understanding the intricate relationship between landscape patterns and ESs, offering a scientific foundation and practical guidance for urban ecological protection, restoration initiatives, and territorial spatial planning. Full article

This study examines the extent to which smart cities are expressions of nationally homogeneous development trends by way of an analysis of their structural characteristics from a multivariate viewpoint. Drawing on data from the International Institute for Management Development IMD Smart City Index 2024, we find a sample of 102 cities across the world clustering along six key dimensions of smartness: mobility, environment, government, economy, people, and living. The aim is to examine if cities within a country have similar profiles and, if so, to what degree such similarity translates to other macro-level institutional, political, and cultural conditions. Our results verify a tight correspondence between city profiles and national contexts, implying that macro-level governance arrangements, policy coordination, and institutional capacity are pivotal in influencing local smart city development. Planned centralised countries possess more uniform city characteristics, while decentralised nations possess more variant urban policies. This study contributes to international debate regarding smart cities by empirically identifying national directions of urban innovation. It offers pragmatic inputs for policymakers that aim to align local efforts with overall sustainable development agendas. Moreover, this study introduces a novel application of Linear Discriminant Analysis (LDA) to classify smart city profiles based on national models. While the analysis yields high classification accuracy, it is important to note that the sample is skewed toward cities from the Global North, potentially limiting the generalisability of the results. Full article

We present a novel derivation of the spacetime metric generated by matter, without invoking Einstein’s field equations. For static sources, the metric arises from a relativistic formulation of D’Alembert’s principle, where the inertial force is treated as a real dynamical entity that exactly compensates gravity. This leads to a conformastatic metric whose geodesic equation—parametrized by proper time—reproduces the relativistic version of Newton’s second law for free fall. To extend the description to moving matter—uniformly or otherwise—we apply a Lorentz transformation to the static metric. The resulting non-static metric accounts for the motion of the sources and, remarkably, matches the weak-field limit of general relativity as obtained from the linearized Einstein equations in the de Donder (or Lorenz) gauge. This approach—at least at Solar System scales, where gravitational fields are weak—is grounded in a new dynamical interpretation of the Equivalence Principle. It demonstrates how gravity can emerge from the relativistic structure of inertia, without postulating or solving Einstein’s equations. Full article

This paper proposes a new proximal iteratively reweighted nuclear norm method for a class of nonconvex and nonsmooth optimization problems. The primary contribution of this work is the incorporation of line search technique based on dimensionality reduction and extrapolation. This strategy overcomes parameter constraints by enabling adaptive dynamic adjustment of the extrapolation/proximal parameters (α_k, β_k, μ_k). Under the Kurdyka–Łojasiewicz framework for nonconvex and nonsmooth optimization, we prove the global convergence and linear convergence rate of the proposed algorithm. Additionally, through numerical experiments using synthetic and real data in matrix completion problems, we validate the superior performance of the proposed method over well-known methods. Full article

In recent years, computational intelligence techniques have significantly contributed to the automation and optimization of trading strategies. Despite the increasing sophistication of predictive models, classical technical indicators such as dual Simple Moving Averages (2-SMA) remain popular due to their simplicity and interpretability. This work proposes an adaptive trading system that combines the 2-SMA strategy with a learning-based metaheuristic optimizer known as the Learning-Based Linear Balancer ($L{B}^2$). The objective is to dynamically adjust the strategy’s parameters to maximize returns in the highly volatile cryptocurrency market. The proposed system is evaluated through simulations using historical data of the BTCUSDT futures contract from the Binance platform, incorporating real-world trading constraints such as transaction fees. The optimization process is validated over 34 training/test splits using overlapping 60-day windows. Results show that the $L{B}^2$-optimized strategy achieves an average return on investment (ROI) of 7.9% in unseen test periods, with a maximum ROI of 17.2% in the best case. Statistical analysis using the Wilcoxon Signed-Rank Test confirms that our approach significantly outperforms classical benchmarks, including Buy and Hold, Random Walk, and non-optimized 2-SMA. This study demonstrates that hybrid strategies combining classical indicators with adaptive optimization can achieve robust and consistent returns, making them a viable alternative to more complex predictive models in crypto-based financial environments. Full article

The selectivity of the colorimetric method for Co(II) determination using the nitroso-R-salt (NRS) in samples with complex matrices has been improved. Interferences caused by Cu(II), Fe(II), Fe(III), Mn(II), Al(III) and Ni(II) ions, which were present in the bioleach ate of lithium-ion batteries, have been solved through the sequential addition of masking agents: acetate, fluoride, ethylenediaminetetraacetic acid (EDTA), and strong acids (H₂SO₄). The absorbance of the NRS-Co(II) complex was typically measured at 525 nm, but it was also studied at 550 nm due to minimal interferences observed at 550 nm. The sequence of the masking agent’s addition showed a significant influence on the interference effect. The optimal sequence was sample, acetate–acetic acid buffer solution with dissolved fluoride, NRS, EDTA and H₂SO₄. The proposed method demonstrated robust performance at 550 nm, with a relative standard deviation (RSD) around 2%, and good accuracy (RV% around 100%). The limit of detection (LoD) was 0.1 mg L⁻¹ and the limit of quantification (LoQ) was 0.3 mg L⁻¹. The linear range extended up to 15 mg L⁻¹ (R² = 0.998). Real samples analyzed using the optimized method showed no significant differences when compared to results from atomic absorption spectroscopy, confirming its reliability. Full article

Background/Objectives: Although people with schizophrenia appear to experience emotions like healthy individuals, previous studies suggest that their ability to engage in empathic emotional responses might be impaired. As per our knowledge, no studies in the Arab world have investigated empathy in patients with schizophrenia, which is likely due to the lack of valid and reliable measures to assess the empathy construct among Arabic-speaking people. The aim of this research is to validate the Arabic version of the Pictorial Empathy Test (PET) in patients with schizophrenia from Lebanon. Methods: A two-month cross-sectional study was carried out at the Psychiatric Hospital of the Cross during January and February of 2024. The average age of the 113 participants in this study was 57.52 ± 10.35 years and 63.5% of them were men. Data were collected through in-person interviews. A confirmatory factor analysis (CFA) was conducted using SPSS AMOS version 29. Parameter estimation utilized the maximum likelihood approach. In order to examine sex invariance in PET scores, a multi-group CFA was conducted. Measurement invariance was assessed across configural, metric, and scalar levels. Evidence of invariance was determined based on the criteria: ΔCFI ≤ 0.010, ΔRMSEA ≤ 0.015, or ΔSRMR ≤ 0.010. Results: CFA revealed that the Arabic PET exhibited a unidimensional factor structure. The PET demonstrated solid internal consistency (ω = 0.93, α = 0.93). Measurement invariance testing confirmed that the scale performed equally well across sexes. A linear regression analysis found that female sex and higher levels of alexithymia were significantly correlated with lower levels of affective empathy. Conclusions: The findings indicate that the Arabic version of the PET is a reliable and valid tool for measuring affective empathy in Arabic-speaking patients with schizophrenia. The culturally adapted and validated Arabic PET would help detect affective empathy deficits, design and implement context-tailored interventions, and encourage future research in this area in the Arab region. Future research should aim to validate the PET against behavioral tasks like the Empathic Accuracy Task to improve its ecological validity. Full article

This study delves into the response characteristics of core grounding current signals in power transformers across different operating conditions, aiming to enhance the accuracy of transformer condition assessment. Existing detection technologies often rely on single-parameter methods, which fall short in providing a comprehensive evaluation of transformer conditions. To address this limitation, this research develops a wideband circuit model based on multi-conductor transmission line theory and backed by experimental validation. The model systematically investigates the response mechanisms of core grounding current to various electrical stresses, including impulse voltages, power-frequency harmonics, and partial discharges. The findings reveal distinct response characteristics of core grounding current under different stresses. Under impulse voltage excitation, the core current exhibits high-frequency oscillatory decay with characteristics linked to voltage waveform parameters. In harmonic conditions, the current spectrum shows linear correspondence with excitation voltages, with no resonance below 1 kHz. Partial discharges induce high-frequency oscillations in the grounding current due to multi-resonant networks formed by distributed winding-core parameters. This study establishes a new theoretical framework for transformer condition assessment based on core grounding current analysis, offering critical insights for optimizing detection technologies and overcoming the limitations of traditional methods. Full article

Polycyclic aromatic hydrocarbon (PAH) derivatives, specifically azaarenes and nitrated and oxygenated PAHs, are emerging contaminants of concern due to their increased toxicity and persistence compared to the parent PAHs. Despite their toxicity, their simultaneous analysis in complex matrices, such as in fumes emitted from bituminous mixtures, remains challenging due to limitations of conventional analytical techniques. To address this, an advanced methodology was developed using Ultra-High-Performance Liquid Chromatography coupled with High-Resolution Mass Spectrometry (UHPLC-HRMS Orbitrap Eclipse) equipped with an APCI source for the simultaneous identification and quantification of 14 PAH derivatives. Chromatographic and ionization parameters were optimized to ensure maximum sensitivity and selectivity. Following ICH Q2(R2) guidelines, the method was validated, demonstrating excellent linearity (R² > 0.99), high mass accuracy (≤5 ppm), strong precision (<15%), and excellent sensitivity. Limits of detection (LODs) ranged from 0.1 µg L⁻¹ to 0.6 µg L⁻¹ and limits of quantification (LOQs) ranged from 0.26 µg L⁻¹ to 1.87 µg L⁻¹. The validated method was successfully applied to emissions from asphalt pavement materials collected on quartz filters under controlled conditions, enabling the identification and quantification of all 14 targeted compounds. These results confirm the method’s robustness and suitability for trace-level analysis of PAH derivatives in complex environmental matrices. Full article

Nanodiamonds in water can generate solvated electrons under ultraviolet (UV) excitation, but UV light constitutes only a small portion of solar energy. To harvest solar energy in the visible range, we investigate band gap reduction via surface amino functionalization and examine its impact on photo-excited charge transfer to water. Adamantane, the smallest nanodiamond, is used as a model due to its electron emission properties. Liquid water is first represented using water dimers and then complete solvation shell structures surrounding the adamantane. By systematically analyzing different functionalized adamantane structures, we find that nitrogen serves as the primary electron donor to nearby water molecules. Furthermore, the negative electron affinity of adamantane, which determines its emission capability, is preserved with half of the amino group functionalization on the surface. Our findings motivate further experimental verification using nanodiamonds with amino-functionalized surfaces. Full article

Addressing the lack of dynamic monitoring methods for assessing the combined toxicity of mixed pesticides, this study developed a fluorescent probe, CCHL, specifically responsive to leucine aminopeptidase (LAP). The probe utilized Cy7-COOH (CCH) as the fluorophore, with fluorescence recovery triggered by enzymatic hydrolysis. Spectral characterization confirmed a linear response between the probe and LAP activity within a concentration range of 0–0.9 μg/mL (R² = 0.992), along with excellent selectivity in the presence of coexisting biomolecules. Application experiments demonstrated that the combination of chlorfenapyr and beta-cyfluthrin significantly reduced LAP activity, revealing a notable antagonistic effect. The novel sensing strategy developed here provides a real-time, visualized analytical tool for evaluating the combined effects of mixed pollutants, demonstrating significant potential for environmental toxicology monitoring. Full article

Self-regulatory foraging behavior in honey bees (Apis mellifera) was investigated using the framework of Perceptual Control Theory (PCT). We developed a PCT-based model to describe how bees maintain goal-directed behavior, specifically targeting a sucrose-rich feeding site while overcoming a wind disturbance. In a controlled experiment, we found that 13 of 14 bees could successfully adjust their flight paths to overcome the disturbance and consistently reach the feeding target. While they demonstrated a great deal of individual variability regarding how they overcame the wind across experimental trials, they did so by finally adopting a headwind (i.e., flying into the wind) approach pattern rather than tailwind or crosswind approach patterns. These results support the application of PCT to the study of behavior in honey bees, which can be regarded as self-regulative (i.e., non-linear and dynamic) rather than as linear sequences of inputs and outputs. Given that such dynamic models are concerned with the functions or purposes of behavior, they may also be classified as teleonomic. Full article